How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
PyTorch implementation of Noisy Natural Gradient as Variational Inference.
The main features of wlwkgus/noisynaturalgradient are: Computer Vision Research, 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… jakezhaojb/arae — Code for the paper "Adversarially Regularized Autoencoders (ICML 2018)" by Zhao, Kim, Zhang, Rush and LeCun. 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… jnhwkim/ban-vqa — ⚠️ Regrettably, I cannot perform maintenance due to the loss of the materials. I'm archiving this repository for…
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"