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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
Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning
The main features of arunmallya/packnet are: Computer Vision Research, Continual Learning Frameworks, Model Implementations.
Open-source alternatives to arunmallya/packnet include: dmitryulyanov/deep-image-prior — This project is an unsupervised image restoration tool that uses a convolutional neural network as a structural prior… irhumshafkat/r2plus1d-pytorch — PyTorch implementation of the R2Plus1D convolution based ResNet architecture described in the paper "A Closer Look at… arunmallya/piggyback — Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights. carpedm20/enas-pytorch — PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing". facebookresearch/gradientepisodicmemory — Continuum Learning with GEM: Gradient Episodic Memory. jakezhaojb/arae — Code for the paper "Adversarially Regularized Autoencoders (ICML 2018)" by Zhao, Kim, Zhang, Rush and LeCun.