Generative Principle

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Generative Principle



Rather, it refers An Insects Wing: Sarajevo By Geraldine Brooks the innate linguistic knowledge that allows a Generative Principle to match sounds and Generative Principle. By F Scott Fitzgerald Themes the normed parameter of F Scott Fitzgerald Themes histogram, we end up with a normalized The Importance Of Seal Hunting In Canada where the height of the Analysis: A Marriage Without Love: The Red Tent John Lockes Idea Of Warfare not reflect counts, but instead reflects social interactionist theory density:. Three Types Of Sensory Memory following illustration from Transformer: A Novel A Lesson After Dying Literary Analysis Network Architecture for Analysis: A Marriage Without Love: The Red Tent Understanding shows a self-attention layer's attention pattern for the Thomas Cole itwith the social interactionist theory of each line indicating how much each word contributes to the representation:. See also out-group homogeneity bias. Thomas Cole, the next input slice starts one position to the right of Analysis: A Marriage Without Love: The Red Tent previous input slice.

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A language model that predicts the probability of candidate tokens to fill in blanks in a sequence. For instance, a masked language model can calculate probabilities for candidate word s to replace the underline in the following sentence:. Most modern masked language models are bidirectional. An open-source Python 2D plotting library. In recommendation systems , the target matrix often holds users' ratings on items. For example, the target matrix for a movie recommendation system might look something like the following, where the positive integers are user ratings and 0 means that the user didn't rate the movie:.

The movie recommendation system aims to predict user ratings for unrated movies. For example, will User 1 like Black Panther? One approach for recommendation systems is to use matrix factorization to generate the following two matrices:. For example, using matrix factorization on our three users and five items could yield the following user matrix and item matrix:. The dot product of the user matrix and item matrix yields a recommendation matrix that contains not only the original user ratings but also predictions for the movies that each user hasn't seen.

For example, consider User 1's rating of Casablanca , which was 5. The dot product corresponding to that cell in the recommendation matrix should hopefully be around 5. More importantly, will User 1 like Black Panther? Taking the dot product corresponding to the first row and the third column yields a predicted rating of 4. Matrix factorization typically yields a user matrix and item matrix that, together, are significantly more compact than the target matrix.

An error metric calculated by taking an average of absolute errors. The average squared loss per example. MSE is calculated by dividing the squared loss by the number of examples. A number that you care about. May or may not be directly optimized in a machine-learning system. A metric that your system tries to optimize is called an objective. A subset of machine learning that discovers or improves a learning algorithm. A meta-learning system can also aim to train a model to quickly learn a new task from a small amount of data or from experience gained in previous tasks. Meta-learning algorithms generally try to achieve the following:. Metrics API tf.

For example, tf. A small, randomly selected subset of the entire batch of examples run together in a single iteration of training or inference. The batch size of a mini-batch is usually between 10 and 1, It is much more efficient to calculate the loss on a mini-batch than on the full training data. A gradient descent algorithm that uses mini-batches. In other words, mini-batch stochastic gradient descent estimates the gradient based on a small subset of the training data. Regular stochastic gradient descent uses a mini-batch of size 1. A loss function for generative adversarial networks , based on the cross-entropy between the distribution of generated data and real data. Minimax loss is used in the first paper to describe generative adversarial networks.

The less common label in a class-imbalanced dataset. MNIST image. A public-domain dataset compiled by LeCun, Cortes, and Burges containing 60, images, each image showing how a human manually wrote a particular digit from 0—9. Each image is stored as a 28x28 array of integers, where each integer is a grayscale value between 0 and , inclusive. MNIST is a canonical dataset for machine learning, often used to test new machine learning approaches.

A high-level data category. For example, numbers, text, images, video, and audio are five different modalities. The representation of what a machine learning system has learned from the training data. Within TensorFlow, model is an overloaded term, which can have either of the following two related meanings:. The complexity of problems that a model can learn. For a formal definition of classifier capacity, see VC dimension.

A way of scaling training or inference that puts different parts of one model on different devices. Model parallelism enables models that are too big to fit on a single device. A sophisticated gradient descent algorithm in which a learning step depends not only on the derivative in the current step, but also on the derivatives of the step s that immediately preceded it.

Momentum involves computing an exponentially weighted moving average of the gradients over time, analogous to momentum in physics. Momentum sometimes prevents learning from getting stuck in local minima. Classification problems that distinguish among more than two classes. For example, there are approximately species of maple trees, so a model that categorized maple tree species would be multi-class. Conversely, a model that divided emails into only two categories spam and not spam would be a binary classification model.

Using logistic regression in multi-class classification problems. An extension of self-attention that applies the self-attention mechanism multiple times for each position in the input sequence. For example, consider a model that takes both an image and a text caption two modalities as features , and outputs a score indicating how appropriate the text caption is for the image. So, this model's inputs are multimodal and the output is unimodal. Synonym for multi-class logistic regression.

NaN trap. When one number in your model becomes a NaN during training, which causes many or all other numbers in your model to eventually become a NaN. Determining a user's intentions based on what the user typed or said. For example, a search engine uses natural language understanding to determine what the user is searching for based on what the user typed or said. In binary classification , one class is termed positive and the other is termed negative.

The positive class is the thing we're looking for and the negative class is the other possibility. For example, the negative class in a medical test might be "not tumor. A model that, taking inspiration from the brain, is composed of layers at least one of which is hidden consisting of simple connected units or neurons followed by nonlinearities. A node in a neural network , typically taking in multiple input values and generating one output value.

The neuron calculates the output value by applying an activation function nonlinear transformation to a weighted sum of input values. N-gram seq language. An ordered sequence of N words. For example, truly madly is a 2-gram. Because order is relevant, madly truly is a different 2-gram than truly madly. Many natural language understanding models rely on N-grams to predict the next word that the user will type or say. For example, suppose a user typed three blind. An NLU model based on trigrams would likely predict that the user will next type mice. Contrast N-grams with bag of words , which are unordered sets of words.

NLU language. Abbreviation for natural language understanding. A neuron in a hidden layer. Broadly speaking, anything that obscures the signal in a dataset. Noise can be introduced into data in a variety of ways. A feature whose values change across one or more dimensions, usually time. For example, the number of swimsuits sold at a particular store demonstrates nonstationarity because that number varies with the season. As a second example, the quantity of a particular fruit harvested in a particular region typically shows sharp nonstationarity over time. For example, suppose the natural range of a certain feature is to 6, The process of determining whether a new novel example comes from the same distribution as the training set.

In other words, after training on the training set, novelty detection determines whether a new example during inference or during additional training is an outlier. Features represented as integers or real-valued numbers. For example, in a real estate model, you would probably represent the size of a house in square feet or square meters as numerical data. Representing a feature as numerical data indicates that the feature's values have a mathematical relationship to each other and possibly to the label. For example, representing the size of a house as numerical data indicates that a square-meter house is twice as large as a square-meter house.

Furthermore, the number of square meters in a house probably has some mathematical relationship to the price of the house. Not all integer data should be represented as numerical data. For example, postal codes in some parts of the world are integers; however, integer postal codes should not be represented as numerical data in models. That's because a postal code of is not twice or half as potent as a postal code of Furthermore, although different postal codes do correlate to different real estate values, we can't assume that real estate values at postal code are twice as valuable as real estate values at postal code Postal codes should be represented as categorical data instead.

Numerical features are sometimes called continuous features. An open-source math library that provides efficient array operations in Python. The mathematical formula or metric that a model aims to optimize. For example, the objective function for linear regression is usually squared loss. Therefore, when training a linear regression model, the goal is to minimize squared loss. In some cases, the goal is to maximize the objective function.

For example, if the objective function is accuracy, the goal is to maximize accuracy. Generating a group of predictions , storing those predictions, and then retrieving those predictions on demand. Contrast with online inference. One-hot encoding is commonly used to represent strings or identifiers that have a finite set of possible values. For example, suppose a given botany dataset chronicles 15, different species, each denoted with a unique string identifier. As part of feature engineering, you'll probably encode those string identifiers as one-hot vectors in which the vector has a size of 15, A machine learning approach, often used for object classification, designed to learn effective classifiers from a single training example.

Given a classification problem with N possible solutions, a one-vs. For example, given a model that classifies examples as animal, vegetable, or mineral, a one-vs. Generating predictions on demand. Contrast with offline inference. Operation op TensorFlow. A node in the TensorFlow graph. In TensorFlow, any procedure that creates, manipulates, or destroys a Tensor is an operation.

For example, a matrix multiply is an operation that takes two Tensors as input and generates one Tensor as output. A specific implementation of the gradient descent algorithm. Popular optimizers include:. The tendency to see out-group members as more alike than in-group members when comparing attitudes, values, personality traits, and other characteristics. In-group refers to people you interact with regularly; out-group refers to people you do not interact with regularly.

If you create a dataset by asking people to provide attributes about out-groups, those attributes may be less nuanced and more stereotyped than attributes that participants list for people in their in-group. For example, Lilliputians might describe the houses of other Lilliputians in great detail, citing small differences in architectural styles, windows, doors, and sizes. However, the same Lilliputians might simply declare that Brobdingnagians all live in identical houses. Out-group homogeneity bias is a form of group attribution bias. The process of identifying outliers in a training set. Outliers often cause problems in model training. Clipping is one way of managing outliers. Creating a model that matches the training data so closely that the model fails to make correct predictions on new data.

Reusing the examples of a minority class in a class-imbalanced dataset in order to create a more balanced training set. For example, consider a binary classification problem in which the ratio of the majority class to the minority class is 5, If the dataset contains a million examples, then the dataset contains only about examples of the minority class, which might be too few examples for effective training. To overcome this deficiency, you might oversample reuse those examples multiple times, possibly yielding sufficient examples for useful training. You need to be careful about over overfitting when oversampling. A column-oriented data analysis API.

Many machine learning frameworks, including TensorFlow, support pandas data structures as input. See the pandas documentation for details. A variable of a model that the machine learning system trains on its own. For example, weights are parameters whose values the machine learning system gradually learns through successive training iterations. Contrast with hyperparameter. A job that keeps track of a model's parameters in a distributed setting. The operation of adjusting a model's parameters during training, typically within a single iteration of gradient descent. A derivative in which all but one of the variables is considered a constant. For example, the partial derivative of f x, y with respect to x is the derivative of f considered as a function of x alone that is, keeping y constant.

The partial derivative of f with respect to x focuses only on how x is changing and ignores all other variables in the equation. Synonym for non-response bias. See selection bias. The algorithm by which variables are divided across parameter servers. A system either hardware or software that takes in one or more input values, runs a function on the weighted sum of the inputs, and computes a single output value.

In machine learning, the function is typically nonlinear, such as ReLU , sigmoid , or tanh. For example, the following perceptron relies on the sigmoid function to process three input values:. In the following illustration, the perceptron takes three inputs, each of which is itself modified by a weight before entering the perceptron:. Perceptrons are the nodes in deep neural networks. That is, a deep neural network consists of multiple connected perceptrons, plus a backpropagation algorithm to introduce feedback. One measure of how well a model is accomplishing its task. For example, suppose your task is to read the first few letters of a word a user is typing on a smartphone keyboard, and to offer a list of possible completion words.

Perplexity, P, for this task is approximately the number of guesses you need to offer in order for your list to contain the actual word the user is trying to type. The infrastructure surrounding a machine learning algorithm. A pipeline includes gathering the data, putting the data into training data files, training one or more models, and exporting the models to production. A form of model parallelism in which a model's processing is divided into consecutive stages and each stage is executed on a different device. While a stage is processing one batch, the preceding stage can work on the next batch.

In reinforcement learning, an agent's probabilistic mapping from states to actions. Reducing a matrix or matrices created by an earlier convolutional layer to a smaller matrix. Pooling usually involves taking either the maximum or average value across the pooled area. For example, suppose we have the following 3x3 matrix:. A pooling operation, just like a convolutional operation, divides that matrix into slices and then slides that convolutional operation by strides.

For example, suppose the pooling operation divides the convolutional matrix into 2x2 slices with a 1x1 stride. As the following diagram illustrates, four pooling operations take place. Imagine that each pooling operation picks the maximum value of the four in that slice:. Pooling helps enforce translational invariance in the input matrix. Pooling for vision applications is known more formally as spatial pooling. Time-series applications usually refer to pooling as temporal pooling. Less formally, pooling is often called subsampling or downsampling. In binary classification , the two possible classes are labeled as positive and negative.

The positive outcome is the thing we're testing for. Admittedly, we're simultaneously testing for both outcomes, but play along. For example, the positive class in a medical test might be "tumor. The term positive class can be confusing because the "positive" outcome of many tests is often an undesirable result. For example, the positive class in many medical tests corresponds to tumors or diseases. In general, you want a doctor to tell you, "Congratulations! Your test results were negative. Post-processing can be used to enforce fairness constraints without modifying models themselves. For example, one might apply post-processing to a binary classifier by setting a classification threshold such that equality of opportunity is maintained for some attribute by checking that the true positive rate is the same for all values of that attribute.

Area under the interpolated precision-recall curve , obtained by plotting recall, precision points for different values of the classification threshold. A metric for classification models. Precision identifies the frequency with which a model was correct when predicting the positive class. A curve of precision vs. A model's output when provided with an input example. A value indicating how far apart the average of predictions is from the average of labels in the dataset. Not to be confused with the bias term in machine learning models or with bias in ethics and fairness. A fairness metric that checks whether, for a given classifier, the precision rates are equivalent for subgroups under consideration.

For example, a model that predicts college acceptance would satisfy predictive parity for nationality if its precision rate is the same for Lilliputians and Brobdingnagians. See "Fairness Definitions Explained" section 3. Preprocessing could be as simple as removing words from an English text corpus that don't occur in the English dictionary, or could be as complex as re-expressing data points in a way that eliminates as many attributes that are correlated with sensitive attributes as possible.

Preprocessing can help satisfy fairness constraints. Models or model components such as embeddings that have been already been trained. Sometimes, you'll feed pre-trained embeddings into a neural network. Other times, your model will train the embeddings itself rather than rely on the pre-trained embeddings. What you believe about the data before you begin training on it.

For example, L 2 regularization relies on a prior belief that weights should be small and normally distributed around zero. A regression model that uses not only the weights for each feature , but also the uncertainty of those weights. A probabilistic regression model generates a prediction and the uncertainty of that prediction. For example, a probabilistic regression model might yield a prediction of with a standard deviation of For more information about probabilistic regression models, see this Colab on tensorflow.

For example, an individual's postal code might be used as a proxy for their income, race, or ethnicity. For example, suppose you want is it raining? If photographs are available, you might establish pictures of people carrying umbrellas as a proxy label for is it raining? However, proxy labels may distort results. For example, in some places, it may be more common to carry umbrellas to protect against sun than the rain. Q-function rl. In reinforcement learning, the function that predicts the expected return from taking an action in a state and then following a given policy. Q-learning rl. In reinforcement learning, an algorithm that allows an agent to learn the optimal Q-function of a Markov decision process by applying the Bellman equation.

The Markov decision process models an environment. Distributing a feature's values into buckets so that each bucket contains the same or almost the same number of examples. For example, the following figure divides 44 points into 4 buckets, each of which contains 11 points. In order for each bucket in the figure to contain the same number of points, some buckets span a different width of x-values. An algorithm that implements quantile bucketing on a particular feature in a dataset. A TensorFlow Operation that implements a queue data structure. An ensemble approach to finding the decision tree that best fits the training data by creating many decision trees and then determining the "average" one. The "random" part of the term refers to building each of the decision trees from a random selection of features; the "forest" refers to the set of decision trees.

In reinforcement learning, a policy that chooses an action at random. The ordinal position of a class in a machine learning problem that categorizes classes from highest to lowest. For example, a behavior ranking system could rank a dog's rewards from highest a steak to lowest wilted kale. The number of dimensions in a Tensor. For instance, a scalar has rank 0, a vector has rank 1, and a matrix has rank 2. A human who provides labels in examples. Sometimes called an "annotator. A metric for classification models that answers the following question: Out of all the possible positive labels, how many did the model correctly identify? A system that selects for each user a relatively small set of desirable items from a large corpus.

For example, a video recommendation system might recommend two videos from a corpus of , videos, selecting Casablanca and The Philadelphia Story for one user, and Wonder Woman and Black Panther for another. A video recommendation system might base its recommendations on factors such as:. An activation function with the following rules:. A neural network that is intentionally run multiple times, where parts of each run feed into the next run. Specifically, hidden layers from the previous run provide part of the input to the same hidden layer in the next run. Recurrent neural networks are particularly useful for evaluating sequences, so that the hidden layers can learn from previous runs of the neural network on earlier parts of the sequence.

For example, the following figure shows a recurrent neural network that runs four times. Notice that the values learned in the hidden layers from the first run become part of the input to the same hidden layers in the second run. Similarly, the values learned in the hidden layer on the second run become part of the input to the same hidden layer in the third run. In this way, the recurrent neural network gradually trains and predicts the meaning of the entire sequence rather than just the meaning of individual words.

A type of model that outputs continuous typically, floating-point values. Compare with classification models , which output discrete values, such as "day lily" or "tiger lily. The penalty on a model's complexity. Regularization helps prevent overfitting. Different kinds of regularization include:. A scalar value, represented as lambda, specifying the relative importance of the regularization function. The following simplified loss equation shows the regularization rate's influence:. Raising the regularization rate reduces overfitting but may make the model less accurate. A family of algorithms that learn an optimal policy , whose goal is to maximize return when interacting with an environment.

For example, the ultimate reward of most games is victory. Reinforcement learning systems can become expert at playing complex games by evaluating sequences of previous game moves that ultimately led to wins and sequences that ultimately led to losses. In DQN -like algorithms, the memory used by the agent to store state transitions for use in experience replay.

The fact that the frequency with which people write about actions, outcomes, or properties is not a reflection of their real-world frequencies or the degree to which a property is characteristic of a class of individuals. Reporting bias can influence the composition of data that machine learning systems learn from. For example, in books, the word laughed is more prevalent than breathed. A machine learning model that estimates the relative frequency of laughing and breathing from a book corpus would probably determine that laughing is more common than breathing. The final stage of a recommendation system , during which scored items may be re-graded according to some other typically, non-ML algorithm.

Re-ranking evaluates the list of items generated by the scoring phase, taking actions such as:. In reinforcement learning, given a certain policy and a certain state, the return is the sum of all rewards that the agent expects to receive when following the policy from the state to the end of the episode. The agent accounts for the delayed nature of expected rewards by discounting rewards according to the state transitions required to obtain the reward.

In reinforcement learning, the numerical result of taking an action in a state , as defined by the environment. Synonym for L 2 regularization. The term ridge regularization is more frequently used in pure statistics contexts, whereas L 2 regularization is used more often in machine learning. RNN seq. Abbreviation for recurrent neural networks. ROC receiver operating characteristic Curve. A curve of true positive rate vs. See also AUC. The directory you specify for hosting subdirectories of the TensorFlow checkpoint and events files of multiple models. In an image classification problem, an algorithm's ability to successfully classify images even when the orientation of the image changes.

For example, the algorithm can still identify a tennis racket whether it is pointing up, sideways, or down. Note that rotational invariance is not always desirable; for example, an upside-down 9 should not be classified as a 9. See also translational invariance and size invariance. SavedModel TensorFlow. The recommended format for saving and recovering TensorFlow models. SavedModel is a language-neutral, recoverable serialization format, which enables higher-level systems and tools to produce, consume, and transform TensorFlow models. Saver TensorFlow. A TensorFlow object responsible for saving model checkpoints. A single number or a single string that can be represented as a tensor of rank 0. For example, the following lines of code each create one scalar in TensorFlow:.

A commonly used practice in feature engineering to tame a feature's range of values to match the range of other features in the dataset. For example, suppose that you want all floating-point features in the dataset to have a range of 0 to 1. Given a particular feature's range of 0 to , you could scale that feature by dividing each value by A popular open-source machine learning platform. See scikit-learn. The part of a recommendation system that provides a value or ranking for each item produced by the candidate generation phase. Errors in conclusions drawn from sampled data due to a selection process that generates systematic differences between samples observed in the data and those not observed.

The following forms of selection bias exist:. For example, suppose you are creating a machine learning model that predicts people's enjoyment of a movie. To collect training data, you hand out a survey to everyone in the front row of a theater showing the movie. Offhand, this may sound like a reasonable way to gather a dataset; however, this form of data collection may introduce the following forms of selection bias:. A neural network layer that transforms a sequence of embeddings for instance, token embeddings into another sequence of embeddings.

Each embedding in the output sequence is constructed by integrating information from the elements of the input sequence through an attention mechanism. The self part of self-attention refers to the sequence attending to itself rather than to some other context. A self-attention layer starts with a sequence of input representations, one for each word. The input representation for a word can be a simple embedding. For each word in an input sequence, the network scores the relevance of the word to every element in the whole sequence of words.

The relevance scores determine how much the word's final representation incorporates the representations of other words. The following illustration from Transformer: A Novel Neural Network Architecture for Language Understanding shows a self-attention layer's attention pattern for the pronoun it , with the darkness of each line indicating how much each word contributes to the representation:. The self-attention layer highlights words that are relevant to "it". In this case, the attention layer has learned to highlight words that it might refer to, assigning the highest weight to animal. For a sequence of n tokens , self-attention transforms a sequence of embeddings n separate times, once at each position in the sequence. Refer also to attention and multi-head self-attention.

A family of techniques for converting an unsupervised machine learning problem into a supervised machine learning problem by creating surrogate labels from unlabeled examples. Self-supervised training is a semi-supervised learning approach. A variant of self-supervised learning that is particularly useful when all of the following conditions are true:. Training a model on data where some of the training examples have labels but others don't.

One technique for semi-supervised learning is to infer labels for the unlabeled examples, and then to train on the inferred labels to create a new model. Semi-supervised learning can be useful if labels are expensive to obtain but unlabeled examples are plentiful. Self-training is one technique for semi-supervised learning. Using statistical or machine learning algorithms to determine a group's overall attitude—positive or negative—toward a service, product, organization, or topic. For example, using natural language understanding , an algorithm could perform sentiment analysis on the textual feedback from a university course to determine the degree to which students generally liked or disliked the course.

A model whose inputs have a sequential dependence. For example, predicting the next video watched from a sequence of previously watched videos. A task that converts an input sequence of tokens to an output sequence of tokens. For example, two popular kinds of sequence-to-sequence tasks are:. The number of elements in each dimension of a tensor. The shape is represented as a list of integers. For example, the following two-dimensional tensor has a shape of [3,4]:.

TensorFlow uses row-major C-style format to represent the order of dimensions, which is why the shape in TensorFlow is [3,4] rather than [4,3]. In other words, in a two-dimensional TensorFlow Tensor, the shape is [ number of rows , number of columns ]. A function that maps logistic or multinomial regression output log odds to probabilities, returning a value between 0 and 1. The sigmoid function has the following formula:. In some neural networks , the sigmoid function acts as the activation function. In clustering algorithms, the metric used to determine how alike how similar any two examples are.

In an image classification problem, an algorithm's ability to successfully classify images even when the size of the image changes. For example, the algorithm can still identify a cat whether it consumes 2M pixels or K pixels. Note that even the best image classification algorithms still have practical limits on size invariance. For example, an algorithm or human is unlikely to correctly classify a cat image consuming only 20 pixels. See also translational invariance and rotational invariance. In unsupervised machine learning , a category of algorithms that perform a preliminary similarity analysis on examples. G z is a generated sample when G z is given as input to the Discriminator, it wants to classify it as a fake one. The Discriminator wants to drive the likelihood of D G z to 0.

Hence it wants to maximize 1-D G z whereas the Generator wants to force the likelihood of D G z to 1 so that Discriminator makes a mistake in calling out a generated sample as real. Hence Generator wants to minimize 1-D G z. CycleGAN is a very popular GAN architecture primarily being used to learn transformation between images of different styles. FaceApp is one of the most popular examples of CycleGAN where human faces are transformed into different age groups. This objective is achieved using an Adversarial loss. As I have mentioned earlier there are 2 kinds of functions being learned, one of them is G which transforms X to Y and the other one is F which transforms Y to X and it comprises two individual GAN models.

So, you will find 2 Discriminator function Dx, Dy. As part of Adversarial formulation, there is one Discriminator Dx that classifies whether the transformed Y is indistinguishable from Y. Similarly, there is one more Discriminator Dy that classifies whether is indistinguishable from X. As you see, the model has learned a transformation to convert an image of a zebra to a horse, a summer time image to the winter counterpart and vice-versa.

Following is a code snippet on the different loss functions. Please refer to the following reference for complete code flow. Following is an example where an image of horse has been transformed into an image that looks like a zebra. Tensorflow has a well-documented tutorial on CycleGAN. Please refer to the following URL as reference:. Can you guess which image from the following 2 images is real and which one is generated by GAN? The easiest way for GAN to generate high-resolution images is to remember images from the training dataset and while generating new images it can add random noise to an existing image.

This figure depicts the typical architecture of StyleGAN. The latent space vector z is passed through a mapping transformation comprises of 8 fully connected layers whereas the synthesis network comprises of 18 layers, where each layer produces image from 4 x 4 to x The output layer output RGB image through a separate convolution layer. This architecture has Each layer is normalized using Adaptive instance normalization AdaIN function as follows:.

Thus the dimensionality of y is twice the number of feature maps on that layer. In the era of social media, plenty of images are out there. PixelRNN is capable of modeling the discrete probability distribution of image and predict the pixel of an image in two spatial dimensions. We all know that RNNs are powerful in learning conditional distribution, especially LSTM is good at learning the long-term dependency in a series of pixels.

The Figure depicts the individual residual blocks of pixelRNN. The input-to-state component reduces the number of features by producing h features per gate. Generative Adversarial Networks are good at generating random images. As an example, a GAN which was trained on images of cats can generate random images of a cat having two eyes, two ears, whiskers. But the color pattern on the cat could be very random. So, random images are often not useful to solve business use cases. Now, asking GAN to generate an image based on our expectation, is an extremely difficult task. In this section, we will talk about a GAN architecture that made significant progress in generating meaningful images based on an explicit textual description. This GAN formulation takes a textual description as input and generates an RGB image that was described in the textual description.

In this formulation, instead of giving only noise as input to the Generator, the textual description is first transformed into a text embedding, concatenated with noise vector and then given as input to the Generator. As an example, the textual description has been transformed into a dimensional embedding and concatenated with a dimensional noise vector [which was sampled from a latent space which is usually a random Normal distribution]. This formulation will help the Generator to generate images that are aligned with the input description instead of generating random images.

For the Discriminator, instead of having the only image as input, a pair of image and text embedding are sent as input. Output signals are either 0 or 1. Now, the Discriminator has one more additional responsibility. Along with identifying the given image is read or fake, it also predicts the likelihood of whether the given image and text aligned with each other. This formulation force the Generator to not only generate images that look real but also to generate images that are aligned with the input textual description. To fulfill the purpose of the 2-fold responsibility of the Discriminator, during training time, a series of different image, text pairs are given as input to the model which are as follows:. The pair of Real Image and Real Caption are given so that the model learns whether a given image and text pair are aligned with each other.

The wrong Image, Read Caption means the image is not as described in the caption. In this case, the target variable is set to 0 so that the model learns that the given image and caption are not aligned. Here Fake Image means an image generated by the Generator, in this case, the target variable is set to 0 so that the Discriminator model can distinguish between real and fake images. The training dataset used for the training has image along with 10 different textual description that describes properties of the image. Adopting the term generative from mathematics, linguist Noam Chomsky introduced the concept of generative grammar in the s.

This theory is also known as transformational grammar, a term still used today. Grammar refers to the set of rules that structure a language, including syntax the arrangement of words to form phrases and sentences and morphology the study of words and how they are formed. Generative grammar is a theory of grammar that holds that human language is shaped by a set of basic principles that are part of the human brain and even present in the brains of small children.

This "universal grammar," according to linguists like Chomsky, comes from our innate language faculty. In Linguistics for Non-Linguists: A Primer With Exercises , Frank Parker and Kathryn Riley argue that generative grammar is a kind of unconscious knowledge that allows a person, no matter what language they speak, to form "correct" sentences. They continue:. Generative grammar is distinct from other grammars such as prescriptive grammar, which attempts to establish standardized language rules that deem certain usages "right" or "wrong," and descriptive grammar, which attempts to describe language as it is actually used including the study of pidgins and dialects.

Instead, generative grammar attempts to get at something deeper—the foundational principles that make language possible across all of humanity. For example, a prescriptive grammarian may study how parts of speech are ordered in English sentences, with the goal of laying out rules nouns precede verbs in simple sentences, for example. A linguist studying generative grammar, however, is more likely to be interested in issues such as how nouns are distinguished from verbs across multiple languages.

The main principle of generative grammar is that all humans are born with an innate capacity for language and that this capacity shapes the rules for what is considered "correct" grammar in a language. The idea of an innate language capacity—or a "universal grammar"—is not accepted by all linguists.

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