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Neural Network Compression

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보존된 평가 기록: 4

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/jack-willturner/characterising-neural-compression

공식 코드

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

https://github.com/joeshenouda/vv-spaces-nn-width

공식 코드

Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression

https://github.com/juliagusak/musco

공식 코드

MUSCO: Multi-Stage Compression of neural networks

https://github.com/kenny-co/sgd-uap-torch

공식 코드

Robustness and Transferability of Universal Attacks on Compressed Models

https://github.com/maxc01/addtree

공식 코드

Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a Fast Implementation

https://github.com/mbarsbey/implicit-compressibility

공식 코드

Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD

https://github.com/mbarsbey/sgd_comp_gen

공식 코드

Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks

https://github.com/mitscha/strassennets

공식 코드

StrassenNets: Deep Learning with a Multiplication Budget

https://github.com/musab-r/TCN

공식 코드

Teacher-Class Network: A Neural Network Compression Mechanism

https://github.com/ofsoundof/learning_filter_basis

공식 코드

Learning Filter Basis for Convolutional Neural Network Compression

https://github.com/openvinotoolkit/nncf

공식 코드

Neural Network Compression Framework for fast model inference

https://github.com/openvinotoolkit/nncf_pytorch

공식 코드

Neural Network Compression Framework for fast model inference

https://github.com/ososos888/prune-then-distill

공식 코드

Prune Your Model Before Distill It

https://github.com/reddragon/efficient-dl-survey-paper

공식 코드

Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better

https://github.com/seolabcornell/torch2chip

공식 코드

Torch2Chip: An End-to-end Customizable Deep Neural Network Compression and Deployment Toolkit for Prototype Hardware Accelerator Design

https://github.com/shaiqmalik/Teacher-Class

공식 코드

Teacher-Class Network: A Neural Network Compression Mechanism

https://github.com/stanleybak/quantized_nn_backreach

공식 코드

Neural Network Compression of ACAS Xu Early Prototype is Unsafe: Closed-Loop Verification through Quantized State Backreachability

https://github.com/szcompressor/DeepSZ

공식 코드

DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression

https://github.com/toshas/sttp

공식 코드

Spectral Tensor Train Parameterization of Deep Learning Layers

https://github.com/toshas/tbasis

공식 코드

T-Basis: a Compact Representation for Neural Networks

https://github.com/v0lta/wavelet-network-compression

공식 코드

Neural network compression via learnable wavelet transforms

https://github.com/wyze-AI/AdaptiveDistillation

공식 코드

Adaptive Distillation: Aggregating Knowledge from Multiple Paths for Efficient Distillation

https://github.com/yoshitomo-matsubara/torchdistill

공식 코드

torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation

https://github.com/yuhuixu1993/SLQ

공식 코드

Deep Neural Network Compression with Single and Multiple Level Quantization

https://github.com/yuxwind/ExactCompression

공식 코드

Scaling Up Exact Neural Network Compression by ReLU Stability

https://github.com/zihao-wang/sparsity-by-redundancy

공식 코드

spred: Solving $L_1$ Penalty with SGD

https://github.com/zihao-wang/spred

공식 코드

spred: Solving $L_1$ Penalty with SGD

https://github.com/AhrazA/modelcompression-2019

Deep Neural Network Compression for Image Classification and Object Detection

https://github.com/JDAI-CV/dabnn

Forward and Backward Information Retention for Accurate Binary Neural Networks

https://github.com/KarenUllrich/Tutorial_BayesianCompressionForDL

Soft Weight-Sharing for Neural Network Compression

https://github.com/LTH14/FSKD

Few Sample Knowledge Distillation for Efficient Network Compression

https://github.com/NatGr/Master_Thesis

A Closer Look at Structured Pruning for Neural Network Compression

https://github.com/akashrajkn/waffles-and-posteriors

Soft Weight-Sharing for Neural Network Compression

https://github.com/andrijazz/pruning

Learning Sparse Networks Using Targeted Dropout

https://github.com/athulshibu/Rewarded-meta-pruning

Revisiting Random Channel Pruning for Neural Network Compression

https://github.com/cambridge-mlg/variational-shannon-coding

Weightless: Lossy Weight Encoding For Deep Neural Network Compression

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.