awesome-model-compression
papers about model compression
https://github.com/ChanChiChoi/awesome-model-compression
Last synced: 15 days ago
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BLOGS & ATRICLES
2023
LIBRARIES
2023
- NVIDIA TensorRT
- OpenBMB/BMCook
- Tencent/PocketFlow
- Tencent/ncnn - performance neural network inference framework optimized for the mobile platform;
OTHERS
2023
PAPERS
1990
1993
1995
1997
1998
2000
- Using A Neural Network to Approximate An Ensemble of Classifiers
- Using A Neural Network to Approximate An Ensemble of Classifiers
- Ensemble Methods in Machine Learning
2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
- A simple neural network pruning algorithm with application to filter synthesis - 53.. 2001
2006
2011
- Product quantization for nearest neighbor search - 128.
- Improving the speed of neural networks on CPUs
2013
- Predicting parameters in deep learning
- Fast training of convolutional networks through ffts
- Do Deep Nets Really Need to be Deep?
- Fast training of convolutional networks through ffts
- Predicting parameters in deep learning
- Do Deep Nets Really Need to be Deep?
2014
- 1.1 computing’s energy problem (and what we can do about it) - State Circuits Conference Digest of Technical Papers, pages 10–14, 2014.
- Dadiannao: A machinelearning supercomputer
- Speeding up convolutional neural networks with low rank expansions
- Learning with Pseudo-Ensembles
- Compressing Deep Convolutional Networks using Vector Quantization
- Training deep neural networks with low precision multiplications
- Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
- Speeding up convolutional neural networks with low rank expansions
- Efficient and Accurate Approximations of Nonlinear Convolutional Networks
- Flattened convolutional neural networks for feedforward acceleration
- Compressing Deep Convolutional Networks using Vector Quantization
- FitNets: Hints for Thin Deep Nets
- Speeding-up convolutional neural networks using fine-tuned cp-decomposition - v-lebedev/cp-decomposition](https://github.com/vadim-v-lebedev/cp-decomposition); jacobgil/pytorch-tensor-decompositions; medium.com/@keremturgutlu/tensor-decomposition-fast-cnn-in-your-pocket-f03e9b2a6788】
- Training deep neural networks with low precision multiplications
- Learning with Pseudo-Ensembles
- Compressing Deep Convolutional Networks using Vector Quantization
- Dark knowledge
- Speeding up convolutional neural networks with low rank expansions
2015
- High-performance hardware for machine learning
- Sparse convolutional neural networks - 814.
- An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices
- Deep fried convnets - 1483.
- Convolutional neural networks at constrained time cost - 5360.
- Deep Learning with Limited Numerical Precision
- Training binary multilayer neural networks for image classification using expectation backpropagation
- Recurrent Neural Network Training with Dark Knowledge Transfer
- Data-free parameter pruning for deep neural networks
- Distilling Model Knowledge
- Neural networks with few multiplications
- Binaryconnect: Training deep neural networks with binary weights during propagations - 3131.
- 8-bit approximations for parallelism in deep learning
- Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
- Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization - Gang Jiang, Boyang Li, Leonid Sigal, 2015
- Reduced-precision strategies for bounded memory in deep neural nets
- CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android
- Fixed-Point Performance Analysis of Recurrent Neural Networks
- Cross Modal Distillation for Supervision Transfer
- Deep Learning with Limited Numerical Precision
- Distilling the Knowledge in a Neural Network
- Training binary multilayer neural networks for image classification using expectation backpropagation
- Accelerating Very Deep Convolutional Networks for Classification and Detection
- Learning both Weights and Connections for Efficient Neural Networks - willturner/DeepCompression-PyTorch](https://github.com/jack-willturner/DeepCompression-PyTorch)】
- Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding - Compression-AlexNet](https://github.com/songhan/Deep-Compression-AlexNet)】
- Neural networks with few multiplications
- Unifying distillation and privileged information - Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik, 2015
- 8-bit approximations for parallelism in deep learning
- Reduced-precision strategies for bounded memory in deep neural nets
- Net2Net: Accelerating Learning via Knowledge Transfer
- Convolutional neural networks with low-rank regularization
- Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications
- CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android
- Dynamic Capacity Networks
- Fixed-Point Performance Analysis of Recurrent Neural Networks
- Quantized Convolutional Neural Networks for Mobile Devices - wu/quantized-cnn](https://github.com/jiaxiang-wu/quantized-cnn)】
- Binaryconnect: Training deep neural networks with binary weights during propagations
- Learning both weights and connections for efficient neural network
- Binaryconnect: Training deep neural networks with binary weights during propagations - 3131.
- Learning Using Privileged Information: Similarity Control and Knowledge Transfer
- Recurrent Neural Network Training with Dark Knowledge Transfer
- Distilling Model Knowledge
- Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
2016
- Deepburning: automatic generation of fpga-based learning accelerators for the neural network family
- DeepRebirth: A General Approach for Accelerating Deep Neural Network Execution on Mobile Devices
- Face model compression by distilling knowledge from neurons
- Fast algorithms for convolutional neural networks - 4021.
- Dynamic network surgery for efficient dnns
- Learning structured sparsity in deep neural networks.
- Fast convnets using groupwise brain damage
- Sparsifying neural network connections for face recognition - 4864.
- A Simple yet Effective Method to Prune Dense Layers of Neural Networks
- Cambricon-x: An accelerator for sparse neural networks
- fpgaconvnet: A framework for mapping convolutional neural networks on fpgas - Programmable Custom Computing Machines, pages 40–47, 2016.
- Throughput-optimized opencl-based fpga accelerator for large-scale convolutional neural networks - Programmable Gate Arrays, FPGA ’16, 2016.
- Accelerating convolutional neural networks for mobile applications
- Going deeper with embedded fpga platform for convolutional neural network. - Programmable Gate Arrays, FPGA ’16, 2016.
- Switched by input: Power efficient structure for rram-based convolutional neural network
- Fusedlayer cnn accelerators
- Lradnn: High-throughput and energy-efficient deep neural network accelerator using low rank approximation
- Quantized convolutional neural networks for mobile devices
- Cnvlutin: Ineffectual-neuron-free deep neural network computing
- From highlevel deep neural models to fpgas.
- Neurocube: A programmable digital neuromorphic architecture with high-density 3d memory
- Energy-efficient cnn implementation on a deeply pipelined fpga cluster
- Caffeine: towards uniformed representation and acceleration for deep convolutional neural networks - Aided Design, page 12, 2016.
- Fixed point quantization of deep convolutional networks.
- DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices
- DXTK: Enabling Resource-efficient Deep Learning on Mobile and Embedded Devices with the DeepX Toolkit
- MCDNN: An Approximation-Based Execution Framework for Deep Stream Processing Under Resource Constraints
- Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables - ROM. ACM, 2016.
- Bitwise neural networks
- Convolutional neural networks using logarithmic data representation
- Do deep convolutional nets really need to be deep and convolutional?
- Adapting Models to Signal Degradation using Distillation - Chyi Su, Subhransu Maji,2016
- Ristretto: Hardware-oriented approximation of convolutional neural networks.
- Learning Infinite-Layer Networks: Without the Kernel Trick
- Fast, compact, and high quality LSTM-RNN based statistical parametric speech synthesizers for mobile devices
- DSD: Dense-Sparse-Dense Training for Deep Neural Networks
- On the efficient representation and execution of deep acoustic models
- Stealing machine learning models via prediction apis - 618.
- Google's neural machine translation system: Bridging the gap between human and machine translation
- LightRNN: Memory and computation-efficient recurrent neural networks - 4393.
- Ultimate tensorization: compressing convolutional and fc layers alike - TF](https://github.com/timgaripov/TensorNet-TF);[Bihaqo/TensorNet](https://github.com/Bihaqo/TensorNet)】
- Net-trim: Convex pruning of deep neural networks with performance guarantee - 3186.【code:DNNToolBox/Net-Trim-v1】
- Sc-dcnn: Highly-scalable deep convolutional neural network using stochastic computing
- Lcnn: Lookup-based convolutional neural network - 7129.
- Pvanet: Lightweight deep neural networks for real-time object detection - faster-rcnn](https://github.com/sanghoon/pva-faster-rcnn)】
- In teacher we trust: Learning compressed models for pedestrian detection
- Fasttext. zip: Compressing text classification models
- Understanding the impact of precision quantization on the accuracy and energy of neural networks - 1479.
- Finn: A framework for fast, scalable binarized neural network inference
- Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
- Bitwise neural networks
- EIE: Efficient Inference Engine on Compressed Deep Neural Network
- Convolutional neural networks using logarithmic data representation
- Learning Infinite-Layer Networks: Without the Kernel Trick
- Fast, compact, and high quality LSTM-RNN based statistical parametric speech synthesizers for mobile devices
- On the efficient representation and execution of deep acoustic models
- Design of efficient convolutional layers using single intra-channel convolution, topological subdivisioning and spatial" bottleneck" structure
- Pruning Filters for Efficient ConvNets - mingjie/rethinking-network-pruning](https://github.com/Eric-mingjie/rethinking-network-pruning/tree/master/imagenet/l1-norm-pruning)】
- Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
- Loss-aware Binarization of Deep Networks
- The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning
- Aggregated Residual Transformations for Deep Neural Networks
- ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA
- SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
- Deep Networks with Stochastic Depth
- Deep Model Compression: Distilling Knowledge from Noisy Teachers
- Accelerating convolutional neural networks for mobile applications