README.md

My model zoo

A list of all the neural networks I have implemented. All of them are written in TensorFlow 2.x style. Some of them are using the functional API, some others (the most recent ones as I find it way more convenient) are using the subclassing API.

The standalone tick indicates whether all the model can be found in one file, and only has TensorFlow imports.

Model Name	Paper	Original Implementation	My Implementation	Task	Standalone
FocNet	FOCNet: A Fractional Optimal Control Network for Image Denoising	Matlab, MatConvNet	tf-focnet	Image denoising	✔️
MWCNN	Multi-level Wavelet-CNN for Image Restoration	Matlab, MatConvNet	tf-mwcnn	Image denoising, SISR	✔️
DIDN	Deep Iterative Down-Up CNN for Image Denoising	Python, Pytorch	tf-didn	Image denoising	✔️
ELDRN	Exponential linear unit dilated residual network for digital image denoising	❌	tf-eldrn	Image denoising	✔️
U-net	U-Net: Convolutional Networks for Biomedical Image Segmentation	Matlab, Caffe	tf-unet	Any kind of image-to-image problem Originally image segmentation	✔️
TDV	Total Deep Variation for Linear Inverse Problems	Python, Pytorch	tf-tdv	Inverse Problems	✔️
KIKI-net	KIKI-net: Cross-Domain Convolutional Neural Networks for Reconstructing Undersampled Magnetic Resonance Images	❌	fastmri-reproducible-benchmark	MR Image reconstruction	❌
Cascade-net	A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction	Python, Pytorch	fastmri-reproducible-benchmark	MR Image reconstruction	❌
PD-net	Learned Primal-dual Reconstruction	Python, TensorFlow 1.x	fastmri-reproducible-benchmark	MR Image reconstruction Originally CT reconstruction	❌
XPDNet	XPDNet for MRI Reconstruction: an Application to the fastMRI 2020 Brain Challenge	❌	fastmri-reproducible-benchmark	MR Image reconstruction	❌