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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Awesome machine learning model compression research papers, quantization, tools, and learning material.
| Date | Stars |
|---|---|
| 2026-07-31 | 546 |
| 2026-08-04 | 546 |
| 2026-08-06 | 546 |
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# Awesome ML Model Compression [](https://awesome.re) An awesome style list that curates the best machine learning model compression and acceleration research papers, articles, tutorials, libraries, tools and more. PRs are welcome! # Contents - [Papers](#papers) - [General](#general) - [Architecture](#architecture) - [Quantization](#quantization) - [Binarization](#binarization) - [Pruning](#pruning) - [Distillation](#distillation) - [Low Rank Approximation](#low-rank-approximation) - [Offloading](#offloading) - [Parallelism](#parallelism) - [Articles](#articles) - [Howtos](#howtos) - [Assorted](#assorted) - [Reference](#reference) - [Blogs](#blogs) - [Tools](#tools) - [Libraries](#libraries) - [Frameworks](#frameworks) - [Videos](#videos) - [Talks](#talks) - [Training & tutorials](#training--tutorials) --- ## Papers ### General - [A Survey of Model Compression and Acceleration for Deep Neural Networks](https://arxiv.org/abs/1710.09282) - [Model compression as constrained optimization, with application to neural nets. Part I: general framework](https://arxiv.org/abs/1707.01209) - [Model compression as constrained optimization, with application to neural nets. Part II: quantization](https://arxiv.org/abs/1707.04319) - [Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better](https://arxiv.org/abs/2106.08962) - [FP8 Formats for Deep Learning](https://arxiv.org/abs/2209.05433) by NVIDIA, Arm, and Intel, 2022 - FP8 delivered the performance of INT8 with accuracy of FP16. E4M3, a variant of FP8 has the benefits of INT8 with none of the loss in accuracy and throughput. ### Architecture - [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) - [MobileNetV2: Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation](https://arxiv.org/abs/1801.04381) - [Xception: Deep Learning with Depthwise Separable Convolutions](https://arxiv.org/abs/1610.02357) - [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://arxiv.org/abs/1707.01083) - [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://arxiv.org/abs/1602.07360) - [Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video](https://arxiv.org/abs/1709.05943) - [AddressNet: Shift-based Primitives for Efficient Convolutional Neural Networks](https://arxiv.org/abs/1809.08458) - [ResNeXt: Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/abs/1611.05431) - [ResBinNet: Residual Binary Neural Network](https://arxiv.org/abs/1711.01243) - [Residual Attention Network for Image Classification](https://arxiv.org/abs/1704.06904) - [Squeezedet: Unified, small, low power fully convolutional neural networks](https://arxiv.org/abs/1612.01051) - [SEP-Nets: Small and Effective Pattern Networks](https://arxiv.org/abs/1706.03912) - [Dynamic Capacity Networks](https://arxiv.org/abs/1511.07838) - [Learning Infinite Layer Networks Without the Kernel Trick](https://arxiv.org/abs/1606.05316v2) - [Efficient Sparse-Winograd Convolutional Neural Networks](https://openreview.net/pdf?id=r1rqJyHKg) - [DSD: Dense-Sparse-Dense Training for Deep Neural Networks](https://openreview.net/pdf?id=HyoST_9xl) - [Coordinating Filters for Faster Deep Neural Networks](https://arxiv.org/abs/1703.09746v3) - [Deep Networks with Stochastic Depth](https://arxiv.org/abs/1603.09382) ### Quantization - [Quantized Convolutional Neural Networks for Mobile Devices](https://arxiv.org/abs/1512.06473) - [Towards the Limit of Network Quantization](https://arxiv.org/abs/1612.01543) - [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://arxiv.org/abs/1609.07061) - [Compressing Deep Convolutional Networks using Vector Quantization](
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Ikko Eltociear Ashimine · Japan
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Guangxuan Xiao · Thinking Machines Lab · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:69ee406a136ee43b, topic:quantization, topic:model-compression, topic:pruning