Generative AI Course in Chennai

Generative AI is designed to produce new data samples that resemble the training data, including formats like images, text, or audio. Unlike traditional AI models that mostly classify or predict, generative models generate novel outputs, enabling tasks like image synthesis, text generation, and style transfer.

Variational Autoencoders (VAEs) compress input data into a latent space representation and then decode it to recreate the original data. By sampling from this latent space, VAEs can generate new, similar data based on the training examples. They differ from GANs by using a probabilistic approach to model data distributions.

Common issues include mode collapse, where the generator produces limited diversity, and unstable training dynamics. These can be mitigated by techniques like using improved architectures (e.g., Wasserstein GAN), balancing the training steps of the generator and the discriminator, and applying regularization methods.

Transformers leverage self-attention mechanisms to understand relationships within sequences. In generative AI, models such as GPT predict the next token in a sequence, allowing them to generate coherent and contextually relevant text. Their ability to model long-range dependencies makes them particularly well-suited for language generation tasks.

Metrics like the Inception Score (IS) and Fréchet Inception Distance (FID) are commonly used to evaluate generative models by measuring the quality and diversity of the images they produce. For text generation, metrics such as BLEU, ROUGE, and perplexity measure how closely generated text matches reference data and fluency.

Generative AI has the potential to produce deepfakes or spread misinformation, which raises important ethical concerns about its misuse and impact on society. Ethical use requires transparency, proper data handling, bias mitigation, and regulation to prevent harm while leveraging benefits in creative arts, data augmentation, and more.

Autoregressive models generate data step-by-step, predicting each part based on previous outputs (e.g., GPT for text). Autoencoder-based models compress data into latent representations and then reconstruct it (e.g., VAEs), allowing generation by sampling from the latent space.

Latent space is a compressed representation where similar inputs are mapped close together. It allows interpolation, manipulation, and sampling to generate diverse outputs. Navigating this space helps in controlling features of generated content, such as style or attributes.

Diffusion models generate data by gradually adding noise to training data and learning to reverse this process step-by-step to reconstruct the data. This method can produce high-quality images and has recently gained attention for its stability and effectiveness compared to GANs.