The goal of the mini-projects is to implement a Noise2Noise model (Section 7.3 of the course). A Noise2Noise model is an image denoising network trained without a clean reference image. The original paper can be found at https://arxiv.org/abs/1803.04189.
The project has two parts, focusing on two different facets of deep learning. The first one is to build a network that denoises using the PyTorch framework, in particular the torch.nn modules and autograd. The second one is to understand and build a framework, its constituent modules, that are the standard building blocks of deep networks without PyTorch’s autograd.