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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.
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