PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"
This project is an unsupervised image restoration tool that uses a convolutional neural network as a structural prior to reconstruct images from noisy or incomplete data. It functions as a neural network image prior, utilizing the inherent biases of the network architecture to restore pixels without the need for a pre-trained dataset or external learning. The system performs zero-shot image restoration by treating the network architecture itself as a regularization term. It uses a randomly initialized encoder-decoder structure and iterative gradient descent to minimize pixel-wise loss, recove
Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights
Continuum Learning with GEM: Gradient Episodic Memory