yashkant / quantized-nets
Contains code for Binary, Ternary, N-bit Quantized and Hybrid CNNs for low precision experiments.
☆24Updated 6 years ago
Alternatives and similar repositories for quantized-nets:
Users that are interested in quantized-nets are comparing it to the libraries listed below
- Efficient Neural Architecture Search coupled with Quantized CNNs to search for resource efficient and accurate architectures.☆5Updated 6 years ago
- Progressive Neural Architecture Search coupled with Binarized CNNs to search for resource efficient and accurate architectures.☆3Updated 6 years ago
- Reducing the size of convolutional neural networks☆113Updated 7 years ago
- ☆53Updated 6 years ago
- bemova / Deep-Compression-Compressing-Deep-Neural-Networks-with-Pruning-Trained-Quantization-and-HuffmanIt is a pytorch implementation of https://arxiv.org/abs/1510.00149 paper.☆29Updated 6 years ago
- Learning-Recurrent-Binary-Ternary-Weights☆12Updated 6 years ago
- Pytorch Implementation using Binary Weighs and activation.Accuracies are comparable .☆42Updated 5 years ago
- This is a PyTorch implementation of the Scalpel. Node pruning for five benchmark networks and SIMD-aware weight pruning for LeNet-300-100…☆41Updated 6 years ago
- Mayo: Auto-generation of hardware-friendly deep neural networks. Dynamic Channel Pruning: Feature Boosting and Suppression.☆114Updated 5 years ago
- PyTorch implementation of Wide Residual Networks with 1-bit weights by McDonnell (ICLR 2018)☆124Updated 6 years ago
- DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures☆32Updated 4 years ago
- XNOR-Net, with binary gemm and binary conv2d kernels, support both CPU and GPU.☆86Updated 5 years ago
- Codes for AAAI2019 paper: Deep Neural Network Quantization via Layer-Wise Optimization using Limited Training Data☆41Updated 6 years ago
- A PyTorch implementation of the iterative pruning method described in Han et. al. (2015)☆40Updated 6 years ago
- This repository represents training examples for the CVPR 2018 paper "SYQ:Learning Symmetric Quantization For Efficient Deep Neural Netwo…☆31Updated 5 years ago
- Implementation for the paper "Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization"☆74Updated 5 years ago
- 3rd place solution for NeurIPS 2019 MicroNet challenge☆35Updated 5 years ago
- Quantized Neural Networks - networks trained for inference at arbitrary low precision.☆146Updated 7 years ago
- Proximal Mean-field for Neural Network Quantization☆22Updated 5 years ago
- The collection of training tricks of binarized neural networks.☆72Updated 4 years ago
- Official PyTorch Implementation of "Learning Architectures for Binary Networks" (ECCV2020)☆26Updated 4 years ago
- PyTorch implementation of binary neural networks☆44Updated 6 years ago
- ☆74Updated 5 years ago
- Pytorch version for weight pruning for Murata Group's CREST project☆57Updated 7 years ago
- ProxQuant: Quantized Neural Networks via Proximal Operators☆29Updated 6 years ago
- Reproduction of WAGE in PyTorch.☆42Updated 6 years ago
- Code for the ICLR2020 "Training Binary Neural Networks with Real-to-Binary Convolutions☆35Updated 4 years ago
- Binary Convolution Network for faster real-time processing in ASICs☆56Updated 6 years ago
- Train neural networks with joint quantization and pruning on both weights and activations using any pytorch modules☆41Updated 2 years ago
- Codes for Accepted Paper : "MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization" in NeurIPS 2019☆54Updated 4 years ago