Yin and Yang of GenAI – GAN

As we continue to explore the various application of GenerativeAI (or GenAI), it’s important to understand one of it’s critical building blocks – Generative Adversarial Networks (GAN). For AI/ML models to be accurate, it’s very important that the underlying data is classified properly. Data classification starts with data tagging (sometimes called annotation or labelling) - It refers to?the process of adding tags to raw data to indicate to a machine learning model the target responses it needs to predict. Data tagging is a very strenuous process which can also be very time/resource consuming. For example, training an algorithm to classify different species of birds from images would typically require a vast dataset containing millions of bird images with accurate labels indicating the specific species of each bird. Data tagging is a potential inhibitor towards efficiently training the learning models. One of the solutions to data tagging is Generative adversarial networks (GANs) - introduced by Ian Goodfellow and his colleagues in 2014. Explaining GAN using the bird species example - In this semi-supervised learning approach, two neural networks engage in a competitive process to enhance their comprehension of a specific concept. For instance, when it comes to identifying bird species, one network strives to differentiate between authentic bird images and counterfeit ones, while its counterpart seeks to deceive it by generating images that closely resemble birds but are not real. As these two networks engage in this competitive dynamic, each network's representation of a bird becomes progressively more precise and refined.

Another example of GAN is the notoriously infamous “deepfake” videos. A deepfake video leverages two machine learning models. One model generates fake content using a dataset of reference videos, while the other attempts to discern whether the video is a forgery or not. When the second model can no longer distinguish the video as counterfeit, the deepfake likely appears convincing to a human observer. GANs perform better when they have access to extensive datasets. This is why many deepfake videos often features celebrities, as these individuals have numerous videos that GANs can utilize to create real deepfakes.

Let's delve into a more detailed explanation of how GANs operate.

To simplify GAN – let’s take example of chinese philosophy of Yin and Yang - which primarily means opposing yet interrelated, mutually reinforcing dynamics. Both "Yin and Yang" and GANs are built on the idea of duality and balance. In "Yin and Yang," the concept represents the interdependence and balance of opposing forces or elements. In GANs, there is a balance between the generator and discriminator networks, where one tries to generate data, and the other tries to distinguish between real and fake data. The equilibrium reached in GANs reflects a balance between these opposing networks.

A GAN consists of two neural networks:

1. Generator (for simplification - calling this as “Yin”): The Yin network's purpose is to create synthetic data samples that resemble real data. It takes random noise or some other form of input and generates data instances, such as images or audio, from that input. Over time, the Yin (or generator) learns to produce data that is increasingly indistinguishable from real data.
2. Discriminator (for simplification - calling this as “Yang”):The Yang network, on the other hand, acts as a binary classifier. It tries to distinguish between real data samples (from a training dataset) and fake data samples generated by the generator. The yang's (for discriminator's) goal is to become better at differentiating real from fake

(Despite these similarities, it's important to note that "Yin and Yang" is a philosophical and cultural concept deeply rooted in Chinese philosophy, whereas GANs are a specific technology within the field of machine learning. While they share some conceptual similarities related to balance and opposition, their applications and contexts are quite distinct).

GAN at play: The below diagram and explanation helps understand the GAN algorithm:

The training dataset, also known as "The Real Data (X)," is what the generator model (G) aims to replicate. It typically comprises batches of data instances. The generator starts with a "Random Noise Vector (z)," a series of random numbers, as its initial input. Using this vector, the generator creates synthetic examples (denoted as G(z)) designed to be indistinguishable from actual data.

Meanwhile, the Discriminator model (D) has the task of differentiating between the generator-produced data and the real data. It receives both real data (X) and the synthetic data (G(z)) as inputs. Based on these, it makes binary decisions for each data instance, classifying them as either 'real' or 'fake.'

The training process of a Generative Adversarial Network (GAN) is iterative, leveraging the classification errors made by the discriminator. These errors are used to adjust the parameters (weights and biases) of both the discriminator and the generator. The training typically employs backpropagation as the algorithm. This iterative training involves two main cycles:

1. An inner loop focuses on refining the discriminator's parameters. The aim here is to enhance its accuracy in correctly labeling both real and synthetic data.
2. An outer loop where the generator's parameters are adjusted. The objective is to produce data that the discriminator is less likely to identify as synthetic.

In conclusion, Generative Adversarial Networks (GANs) have revolutionized the field of machine learning by introducing a novel approach to generative modeling. Their ability to create realistic data and images has found applications in various domains, from art and entertainment to medical imaging and data augmentation. However, GANs also come with ethical and security challenges, such as the creation of convincing deepfake content, which necessitates responsible use and regulation.

Community contribution using open source AI for building/training models can be explored at https://huggingface.co