jakc4103 / scale-adjusted-trainingLinks
PyTorch implementation of Towards Efficient Training for Neural Network Quantization
☆15Updated 5 years ago
Alternatives and similar repositories for scale-adjusted-training
Users that are interested in scale-adjusted-training are comparing it to the libraries listed below
Sorting:
- PyTorch implementation of EdMIPS: https://arxiv.org/pdf/2004.05795.pdf☆59Updated 5 years ago
- BitSplit Post-trining Quantization☆50Updated 3 years ago
- ☆48Updated 5 years ago
- [ICLR 2022 Oral] F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization☆95Updated 3 years ago
- source code of the paper: Robust Quantization: One Model to Rule Them All☆40Updated 2 years ago
- Post-training sparsity-aware quantization☆34Updated 2 years ago
- Codes for Accepted Paper : "MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization" in NeurIPS 2019☆55Updated 5 years ago
- Code for our paper at ECCV 2020: Post-Training Piecewise Linear Quantization for Deep Neural Networks☆69Updated 3 years ago
- Any-Precision Deep Neural Networks (AAAI 2021)☆61Updated 5 years ago
- [CVPR'20] ZeroQ Mixed-Precision implementation (unofficial): A Novel Zero Shot Quantization Framework☆14Updated 4 years ago
- ☆57Updated 4 years ago
- ☆39Updated 2 years ago
- [NeurIPS 2020] ShiftAddNet: A Hardware-Inspired Deep Network☆74Updated 4 years ago
- Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming☆99Updated 4 years ago
- Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming☆36Updated 2 years ago
- Example for applying Gaussian and Laplace clipping on activations of CNN.☆34Updated 6 years ago
- Code for ICML 2021 submission☆34Updated 4 years ago
- DNN quantization with outlier channel splitting☆113Updated 5 years ago
- The PyTorch implementation of Learned Step size Quantization (LSQ) in ICLR2020 (unofficial)☆136Updated 4 years ago
- ☆46Updated 5 years ago
- ☆76Updated 3 years ago
- ☆47Updated 3 years ago
- ProxQuant: Quantized Neural Networks via Proximal Operators☆29Updated 6 years ago
- ☆44Updated 4 years ago
- ☆25Updated 3 years ago
- ☆43Updated last year
- Pytorch implementation for FAT: learning low-bitwidth parametric representation via frequency-aware transformation☆27Updated 4 years ago
- BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization (ICLR 2021)☆40Updated 4 years ago
- A PyTorch Framework for Efficient Pruning and Quantization for specialized accelerators.☆37Updated 3 years ago
- How Do Adam and Training Strategies Help BNNs Optimization? In ICML 2021.☆60Updated 4 years ago