An implemenet of the Noise2Noise paper (Lehtinen et al., 2018), implementing everything from scratch, such as convolution and backpropagation.
I used 32x32 images from ImageNet with random gaussian noise. The images are available in the form of pickle files for PyTorch. Pretrained weights are also available on this Drive.
A demo Jupyter Notebook is available here.