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awesome-quantization-and-fixed-point-training
Neural Network Quantization & Low-Bit Fixed Point Training For Hardware-Friendly Algorithm Design
https://github.com/A-suozhang/awesome-quantization-and-fixed-point-training
- 1510-Deep Compression
- 1702-Incremental Quantization
- 1802-Model compression via distillation and quantization
- 1810-Post training 4-bit quantization of convolutional networks for rapid-deployment(NIPS 2019)
- 1907-And the Bit Goes Down: Revisiting the Quantization of Neural Networks
- 1511-Fixed Point Quantization of Deep Convolutional Network
- Entropy Constraint Scalar Quantization
- 1611-Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
- 1711-NISP: Pruning Networks using Neuron Importance Score Propagation
- 1805-Retraining-Based Iterative Weight Quantization for Deep Neural Networks
- 1906-Data-Free Quantization through weiht equailization & Bias Correction
- 1603-XNORNet
- 1605-TWN
- 1709-WRPN-Intel-ICLR2018
- 1712-Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 1805-PACT
- 1802-Mixed Precision Training Of ConvNets Using Integer Operations-ICLR2018
- 1805-Accurate & Efficient 2-bit QNN
- 1808-Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss
- 1905-hawq: hessian aware quantization of neuralnetworks with mixed-precision
- 1911-V2
- 1901-Improving Neural Network Quantization with Retraining Outlier Channel Splitting
- Code at - Implemented with Distiller Library
- 1903-Training Quantized Network with Auxiliary Gradient Module
- 1901-Accumulation bit-width Scaling
- 1606-DoReFa
- 1802-WAGE - Training & Inference with Integers in DNN
- 1705-TernGrad
- 1805-Scalable Methods for 8-bit Training of Neural Networks
- 1812-Training Deep Neural Networks with 8-bit Floating Point Numbers
- 1905-mixed precision training with 8-bit floating point
- Towards Unified INT8 Training for Convolutional Neural Network
- 1511-BinaryConnect
- 1602-Binarized Neural Networks
- 1603-XNORNet
- 1605-Ternary Weighted Network
- 1709-WRPN-Intel-ICLR2018
- 1606-DoReFa
- 1709-Flexible Network Binarization with Layer-wise Priority
- 1711-ReBNet: Residual Binarized Neural Network
- 1711-Towards Accurate Binary Convolutional Neural Network
- 1804-Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving
- 1902-Self-Binarizing Networks
- 1906-Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization
- 2001-Least squares binary quantization of neural networks
- 2002-Widening and Squeezing: Towards Accurate and Efficient QNNs
- 1808-Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm
- 1812-Training Competitive Binary Neural Networks from Scratch
- 1909-XNORNet++
- 2001-MeliusNet: Can Binary Neural Networks Achieve MobileNet-level Accuracy?
- 1909-IRNet-Forward and Backward Information Retention for Accurate Binary Neural Networks
- 1812-Per-Tensor-Quantization of BackProp
- 1812-Hybrid 8-bit Training
- 这篇文章
- 代表文章
- PACT
- 这篇
- PACT
- KAIST的这篇文章
- TTQ
- TernGrad
- Towards Unified INT8 Training for Convolutional Neural Network
- Binary Neural Networks: A Survey
- 这篇
- TernGrad
- Post-Training 4bit
- L1BN (Linear BN)
- RangeBN
- BNN
- BinaryConnect
- TernaryNet(TWN)
- XNorNet
- ABCNet
- WRPN-Intel-ICLR2018
- DoReFaNet
- TTQ(Trained Ternary Quantization)
- Simultaneously Optimizing Weight and Quantizer of Ternary Neural Network using Truncated Gaussian Approximation
- Training Competitive Binary Neural Networks from Scratch
- Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- PACT
- Incremental Quantization
- Model compression via distillation and quantization
- Training Deep Neural Networks with 8-bit Floating Point Numbers
- Training Quantized Nets: A Deeper Understanding
- Towards The Limits Of Network Quantization
- Accumulation bit-width Scaling
- Per-Tensor-Quantization of BackProp
- Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets
- An Empirical study of Binary Neural Networks' Optimisation
- Scalable Methods for 8-bit Training of Neural Networks | Part 5
- ernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
- Deep Learning with Low Precision by Half-wave Gaussian Quantization
- Learning low-precision neural networks without Straight-Through Estimator (STE)
- SWALP: Stochastic Weight Averaging in Low-Precision Training
- Analysis Of Quantized MOdels-ICLR2019
- Training Quantized Network with Auxiliary Gradient Module
- Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss
- ReLeQ: An Automatic Reinforcement Learning Approach for Deep Quantization of Neural Networks
- And the Bit Goes Down: Revisiting the Quantization of Neural Networks
- Ternary MobileNets via Per-Layer Hybrid Filter Banks
- Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations
- MoBiNet: A Mobile Binary Network for Image Classification
- Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1
- Tensorflow Lite
- Post-Training-Quantize的工具
- Quantize-Aware Training
- Quantization Tool
- Awesome-Model-Compression
- Blog N0.0
- TensorBoard Lite Doc
- Distiller Doc
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