Generative Adversarial Networks
Definition: Generative Adversarial Networks (GANs) consist of two neural networks, a generator and a discriminator, trained simultaneously through competition. The generator creates synthetic data while the discriminator evaluates its authenticity.
Training Process: The generator tries to produce data that can fool the discriminator, who aims to distinguish between real and fake data. This adversarial process continues until the generator produces highly realistic data.
Applications: GANs are used in image generation (e.g., generating realistic photos of people), style transfer (e.g., converting photos into paintings), and even music and videos.
Generative Adversarial Networks (GANs) are among the most innovative and impactful generative AI models. Introduced by Ian Goodfellow and his colleagues in 2014, GANs have revolutionised the field of artificial intelligence by enabling the creation of highly realistic synthetic data. The core idea behind GANs is the adversarial training process, where two neural networks, the generator and the discriminator, compete against each other to improve the quality of generated data.
Architecture of GANs
Generator
Function: The generator's role is to create synthetic data that resembles the actual data. It takes random noise as input and transforms it into data samples through neural network layers.
Objective: The generator aims to produce indistinguishable data from accurate data, effectively "fooling" the discriminator.
Discriminator
Function: The discriminator's job is to distinguish between actual data and the synthetic data produced by the generator. A binary classifier outputs the probability of a given input being real.
Objective: The discriminator aims to correctly classify real and fake data, thereby improving its ability to detect authenticity.
Adversarial Training Process
Game Theory: GANs are trained through a minimax game, where the generator and discriminator have opposing objectives. The generator tries to maximise the discriminator's error while the discriminator minimises its classification error.
Loss Functions: The generator and discriminator are trained using different loss functions. The generator's loss is based on how well it can fool the discriminator, while the discriminator's loss is based on its classification accuracy.
Figure – Generator and Discriminator during a GAN minimax game
Training GANs
Initialisation
Random Noise: The generator starts with random noise as input, gradually transforming it into more realistic data through training.
Weight Initialization: Proper initialisation of weights is crucial to ensure stable training.
Alternating optimisation
Generator Update: In each training iteration, the generator creates a batch of synthetic data, which the discriminator then evaluates. Based on the discriminator's feedback, the generator's parameters are updated to minimise the loss.
Discriminator Update: The discriminator is trained on both real data and the synthetic data produced by the generator. Its parameters are updated to improve its ability to distinguish real from fake data.
Convergence
Balancing Act: Successful training requires a delicate balance between the generator and the discriminator. If one becomes too powerful, the model may fail to learn effectively.
Indicators of Convergence: Convergence is typically indicated by the generator producing data that is increasingly indistinguishable from real data and the discriminator having an approximately 50% accuracy, meaning it is equally likely to classify real and fake data correctly.
Generative Adversarial Networks (GANs) represent a groundbreaking advancement in AI, enabling the creation of realistic synthetic data with wide-ranging applications. By understanding the architecture, training process, and challenges of GANs, we gain insights into their transformative potential and the ongoing research aimed at overcoming their limitations. In the next section, we will explore other generative AI models, such as Variational Autoencoders and Transformer models, to provide a comprehensive understanding of the landscape of generative AI.
Learn more about Generative AI Models, mainly Challenges and Future Directions for each main model, in our article:
https://buildingcreativemachines.substack.com/p/generative-ai-models-challenges-and