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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 PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning
PyTorch implementation of the R2Plus1D convolution based ResNet architecture described in the paper "A Closer Look at Spatiotemporal Convolutions for Action Recognition"
Code for the paper "Adversarially Regularized Autoencoders (ICML 2018)" by Zhao, Kim, Zhang, Rush and LeCun
The main features of jakezhaojb/arae are: Computer Vision Research, Data Augmentation and Imputation, Model Implementations.
Projects with overlapping indexed features include: dmitryulyanov/deep-image-prior — This project is an unsupervised image restoration tool that uses a convolutional neural network as a structural prior… jnhwkim/ban-vqa — ⚠️ Regrettably, I cannot perform maintenance due to the loss of the materials. I'm archiving this repository for… arunmallya/packnet — Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning. carpedm20/enas-pytorch — PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing". irhumshafkat/r2plus1d-pytorch — PyTorch implementation of the R2Plus1D convolution based ResNet architecture described in the paper "A Closer Look at… kenshohara/3d-resnets-pytorch — This project is a PyTorch implementation of 3D residual networks designed for video action recognition. It provides a…