Neural Network

Overview This project is a fun exploration of the neural field and neural radiance field. In the first part, we fit a neural field to a 2D image. In the second part, we fit a neural radiance field to a 3D scene. Rendered Images Rendered Depths Part 1: Fit a Neural Field to a 2D Image In this part, we implement and train a neural field to represent a 2D image. More mathematically, we are trying to fit a neural field that: $$ F: {x, y} \rightarrow {r, g, b} $$ where $x, y$ are the coordinates of the image and $r, g, b$ are the RGB values of the image. ...

Overview This project focuses on implementing a diffusion model to generate images. The diffusion model is a generative model that generates images by iteratively applying a series of transformations to a noise image. The model is trained to generate images that are similar to the training data. The project is divided into two parts. In Part 1, we implement the forward process of the diffusion model and explore different denoising techniques using a pre-trained diffusion model. In Part 2, we train a single-step denoising UNet and extend it to include time-conditioning and class-conditioning. ...