https://bitbucket.org/YanivShu/binary_weighted_networks_public
공식 코드Exact Backpropagation in Binary Weighted Networks with Group Weight Transformations
Papers with Code 아카이브
Neural Network Compression
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/1adrianb/binary-networks-pytorch
공식 코드https://github.com/42shawn/causal-dfq
공식 코드https://github.com/BGUCompSci/WaveletCompressedConvolution
공식 코드https://github.com/BayesWatch/pytorch-prunes
공식 코드A Closer Look at Structured Pruning for Neural Network Compression
https://github.com/ChaofanTao/FAT_Quantization
공식 코드FAT: Learning Low-Bitwidth Parametric Representation via Frequency-Aware Transformation
https://github.com/Hyeji-Kim/ENC
공식 코드https://github.com/IST-DASLab/WoodFisher
공식 코드WoodFisher: Efficient Second-Order Approximation for Neural Network Compression
https://github.com/KarenUllrich/Tutorial-SoftWeightSharingForNNCompression
공식 코드https://github.com/NVlabs/condensa
공식 코드https://github.com/NervanaSystems/distiller
공식 코드Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
https://github.com/ParkLabML/Dirichlet_Pruning
공식 코드https://github.com/ShaowuChen/JointSVD
공식 코드Joint Matrix Decomposition for Deep Convolutional Neural Networks Compression
https://github.com/ShaowuChen/WHC
공식 코드WHC: Weighted Hybrid Criterion for Filter Pruning on Convolutional Neural Networks
https://github.com/SkoltechAI/fewbit
공식 코드Few-Bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction
https://github.com/VainF/Torch-Pruning
공식 코드https://github.com/ZJULearning/COP
공식 코드COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning
https://github.com/Zinoex/certified-neural-approximations
공식 코드https://github.com/albertomarchisio/swifttron
공식 코드SwiftTron: An Efficient Hardware Accelerator for Quantized Transformers
https://github.com/alexandertheus/intra-fusion
공식 코드https://github.com/amanmohanty/idl-nncompress
공식 코드Efficient CNN-LSTM based Image Captioning using Neural Network Compression
https://github.com/amirgholami/ZeroQ
공식 코드https://github.com/apple/ml-spin
공식 코드SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks
https://github.com/cambridge-mlg/miracle
공식 코드Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
https://github.com/codestar12/Parallel-Independent-Blockwise-Distillation
공식 코드Parallel Blockwise Knowledge Distillation for Deep Neural Network Compression
https://github.com/cornell-zhang/dnn-quant-ocs
공식 코드Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
https://github.com/cunningham-lab/cb_and_cc
공식 코드The continuous categorical: a novel simplex-valued exponential family
https://github.com/deep-fry/mayo
공식 코드https://github.com/eclipsess/chip_neurips2021
공식 코드CHIP: CHannel Independence-based Pruning for Compact Neural Networks
https://github.com/fraunhoferhhi/DeepCABAC
공식 코드DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks
https://github.com/haochen-rye/nerv
공식 코드https://github.com/hrcheng1066/awesome-pruning
공식 코드A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations
https://github.com/htqin/IR-Net
공식 코드Forward and Backward Information Retention for Accurate Binary Neural Networks
https://github.com/huawei-noah/Data-Efficient-Model-Compression/tree/master/DAFL
공식 코드https://github.com/hui-po-wang/LM-GC
공식 코드https://github.com/hustvl/pd-quant
공식 코드PD-Quant: Post-Training Quantization based on Prediction Difference Metric
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.