Classification with backbone Resnet and attentions: SE-Channel Attention, BAM - (Spatial Attention, Channel Attention, Joint Attention), CBAM - (Spatial Attention, Channel Attention, Joint Attention)

Pretrained TorchVision models on CIFAR10 dataset (with weights)

Code for visualizing the loss landscape of neural nets

Self-grouping Convolutional Neural Networks, Neural Networks, 2020

Pruning

A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).

It is a pytorch implementation of https://arxiv.org/abs/1510.00149 paper.

A curated list of neural network pruning resources.

LOss-Based SensiTivity rEgulaRization: towards deep sparse neural networks | Neural Networks https://doi.org/10.1016/j.neunet.2021.11.029

Code for CHIP: CHannel Independence-based Pruning for Compact Neural Networks (NeruIPS 2021).

[TPAMI 2024] This is the official repository for our paper: ''Pruning Self-attentions into Convolutional Layers in Single Path''.

A reproduction of PRUNING FILTERS FOR EFFICIENT CONVNETS

Simple python code to prune pytorch model

Pruning Filters For Efficient ConvNets, PyTorch Implementation.

Knowledge distillation from Ensembles of Iterative pruning (BMVC 2020)

[ICLR'21] Neural Pruning via Growing Regularization (PyTorch)

On-the-fly Structured Pruning for PyTorch models. This library implements several attributions metrics and structured pruning utils for neural networks in PyTorch.

Try out different pruning-approaches on lightweight Backbones.

PyTorch Implementation of Weights Pruning

Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Pruning Neural Networks with Taylor criterion in Pytorch

Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (CVPR 2019 Oral)

MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning. In ICCV 2019.

[CVPR 2023] DepGraph: Towards Any Structural Pruning

PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference

Rethinking the Value of Network Pruning (Pytorch) (ICLR 2019)

Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)