Generative Adversarial Networks (GANs). Artificial Intelligence where neural nets play against each other and improve enough to generate something new. Rob Miles explains GANs
Generating Fake faces in photos and videos has become prevalent in last few years (cough) Deep Fakes (cough). It’s come to a point where we can’t tell if photos and videos contain real people. It’s fascinating (and scary?) ! But how did we get here? What is the technology behind these eerily real faces?
The main Technology behind this phenomenon are “Generative Adversarial Networks”. Let’s talk about them.
In recent news, US-based NLP startup, Hugging Face has raised a whopping $40 million in funding. The company is building a large open-source community to help the NLP ecosystem grow. Its transformers library is a python-based library that exposes an API for using a variety of well-known transformer architectures such as BERT, RoBERTa, GPT-2, and DistilBERT. Here is a list of the top alternatives to Hugging Face .
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The main idea is to develop a generative model via an adversarial process. We will discuss what is an adversarial process later. GAN consists of two model. The one is generative model G and the other is discriminative model D. The purpose of a generative model is to generate the closest data as possible for give some input. The purpose of a discriminative model between two classes 0 and 1. 0 meaning the class belongs to Generative output and 1 meaning the class belongs to the true input sample from the original data.
This architecture corresponds to the minmax two-player game. One tries to create conflict over the other. Such networks are called adversarial networks. In the process of creating conflicts, both of them learn to be better and stronger than each other. When the discriminator makes an output of value ½ or 0.5, it implies that the discriminator is not able to distinguish whether the value came from the generator output or the original sample.
Here, the G and D are defined by the multilayered perceptron such that the entire system can be trained with back propagation. The training of the discriminator and generator are done separately.
According to the paper, the generative model can be thought of as analogous to a team of counterfeiters who are trying to produce a fake currency and use them without getting caught.
While, the discriminative model can be thought of as analogous to the Police who are trying to detect the fake currency. Here, both the teams try to improve their methods until the currencies are indistinguishable from the original currency.
Straight from the paper,
To learn the generator’s distribution Pg over data x, we define a prior on input noise variables Pz(z), then represent a mapping to data space as G(z; θg ).
where G is a differentiable function represented by a multilayer perceptron with parameters θ g .
We also define a second multilayer perceptron D(x; θd ) that outputs a single scalar.
Where D(x) represents the probability that x came from the data rather than Pg.
The architecture of GAN can be explained from the following figure.
#generative-adversarial #discriminator #adversarial-network #deep-learning #neural-networks
In 2017, the paper “Attention is all you need”shocked the land of NLP (Natural Language Processing). It was shocking not only because it has a good paper title, but also because it introduced to the world a new model architecture called “Transformer”, which proved to perform much better than the traditional RNN type of networks and paved its way to the state of the art NLP model “BERT”.
This stone cast in the pond of NLP has created ripples in the pond of GANs (Generative Adversarial Networks). Many have been inspired by this and attempt to garner the magic power of attention. But when I first started reading papers about using attention in GANs, it appeared to me there are so many different meanings behind the same “attention” word. In case you are as confused as I was, let me be at your service and shed some light on what people really mean when they say they use “attention” in GANs.
Self-attention in GANs is very similar to the mechanism in the NLP Transformer model. Basically, it addresses the difficulty of the AI model in understanding long-range dependency.
In NLP, the problem arises when there is a long sentence. Take the example of this Oscar Wilde’s quote _“To __live _is the rarest thing in the world. Most people exist, that is all.” The two words in bold (“live” and “exist”) have a relationship, but they are placed far apart from each other which makes it hard for the RNN type of AI model to capture the relationship.
It is almost the same in GANs, most of GANs use CNN structure which is good at capturing local features and may overlook the long-range dependency when it is outside of its receptive field. As a result, it is easy for GANs to generate realistic-looking furs on dogs, but it may make a mistake by generating a dog with 5 legs.
Self-Attention Generative Adversarial Networks(SAGAN) adds a self-attention module to guide the model to look at features at distant portions of the image. In the pictures below, the picture on the left is the generated image, with some sample locations labeled with color dots. The other images showing the corresponding attention map of the locations. I find the most interesting one is the 5th image with the cyan dot. It shows that when the model generates the left ear of the dog, it not only looks at the local region around the left ear but also looks at the right ear.
Visualization of the attention map for the color labeled locations. source
GANs consist of a generator and a discriminator. In the GANs world, they are like two gods eternally at war, where the generator god tirelessly creates, and the discriminator god stands at the side and criticizes how bad these creations are. It may sound like the discriminator is the bad god, but that is not true. It is through these criticisms that the generator god knows how to improve.
If these “criticisms” from discriminator are so helpful, why not we pay more attention to them? Let’s see how this paper “U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation_” _does this.
The project U-GAT-IT tackles a difficult task — converts a human photo into a Japanese anime image. It is difficult because an anime character’s face is vastly different from a real person’s face. Take an example of the pair images below. The anime character on the right is deemed as a good conversion from the person on the left. But is we put ourselves in the computer’s shoes for a moment, we will see that the eyes, nose, and mouth in the two images are very different, the structure and proportion of the face also changes a lot. It is very hard for a computer to know what features to preserve and what to modify.
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Generative Adversarial Networks (GANs) are very successful in generating very sharp and realistic images. This post briefly explains our image generation framework based on GANs to sequentially compose an image scene, breaking down the underlying problem into smaller ones. For an in-depth description, please see our publication: A Layer-Based Sequential Framework for Scene Generation with GANs.
Fig. 1: The proposed image generation process. Given a semantic layout map, our model composes the scene step-by-step. The first row shows the input semantic map and images generated by state-of-the-art baselines.
Fig. 2: GAN framework.
A generative adversarial network (GAN)  is a class of machine learning frameworks. Two neural networks: (i) generator, and (ii) discriminator contest with each other in a game-theoretic scenario. The generator takes a random noise as an input and generates a fake sample. The discriminator attempts to distinguish between samples drawn from the training dataset (real samples e.g hand-written digit images) and samples produced by the generative model (fake samples). This game drives the discriminator to learn to correctly classify samples as real or fake. Simultaneously, the generator attempts to fool the classifier into believing its samples are real. At convergence, the generators samples are indistinguishable from training data. For more details, please see the original paper or this post. GANs can be used for image generation; they are able to learn to generate sharp and realistic image data.
Fig. 3: Different scaling of the foreground object.
The automatic image generation problem has been studied extensively in the GAN literature [2,3,4]. It has mostly been addressed as learning a mapping from a single source, e.g. noise or semantic map, to target, e.g. images of zebras. This formulation sets a major restriction on the ability to control scene elements individually. So for instance, it is difficult to change the appearance or shape of one zebra while keeping the rest of the image scene unaltered. Let’s look at Fig. 3. If we change the object size, the background is also changed even though the input noise is the same for each row.
Our main idea resembles how a landscape painter would first sketch out the overall structure and later embellish the scene gradually with other elements to populate the scene. For example, the painting could start with mountain ranges or rivers as background while trees and animals are added sequentially as foreground instances.
The main objective is broken down into two simpler sub-tasks. First, we generate the background canvas** x0** with the background generator Gbg conditioned on a noise. Second, we sequentially add foreground objects with the foreground generator Gfg to reach the final image xT, which contains the intended T foreground objects on the canvas (T is not fixed). Our model allows user control over the objects to generate, as well as, their category, their location, their shape, and their appearance.
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