AI Club, IIT Madras

The Pre-Epoch built the prerequisites needed to understand diffusion models by revisiting neural networks and probability distributions, then covering CNNs, U-Nets, autoencoders, and Markov chains in depth. On Day 1, participants developed an intuition for generative modelling and diffusion models through data and probability landscapes, forward noising, score-based learning and training, reverse diffusion, and DDPMs. Day 2 focused on architecture and implementation, including modifications to U-Nets for diffusion, conditioning, samplers, latent diffusion, and implementing a diffusion model in code.

A beginner-friendly introduction to neural networks that began with linear and polynomial regression to frame models as learned functions. The session then developed the core building blocks of a neural network like loss functions, weights, biases, and activation functions and showed how forward propagation produces predictions while gradient descent and backpropagation improve the model during training.

Prof. Kaushik Mitra explored how computational imaging can reconstruct and visualize 3D scenes beyond the limits of conventional cameras. The talk covered PRISM3D for severely motion-blurred scenes, PhotonSplat for extreme low-light reconstruction with single-photon sensors, and GANESH for lensless 3D novel-view synthesis, with applications spanning robotics, AR/VR, medical imaging, and wearable devices.