awesome-compression-papers
github.com/chenbong/awesome-compression-papers ↗Paper collection about model compression and acceleration: Pruning, Quantization, Knowledge Distillation, Low Rank Factorization, etc
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202020192018
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2019
- 2019-CVPR-OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks2019-CVPR
- 2019-ICML-Approximated Oracle Filter Pruning for Destructive CNN Width Optimization github2019-ICML
- 2019-NIPS-One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers2019-NIPS
- Accelerate CNN via Recursive Bayesian Pruning2019-ICCV
- Accelerating Convolutional Neural Networks via Activation Map Compression2019-CVPR
- ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks2019-ICCV
2020
- Accelerating Large-Scale Inference with Anisotropic Vector Quantization2020-ICML
- Adaptive Loss-aware Quantization for Multi-bit Networks2020-CVPR
- AdderNet: Do We Really Need Multiplications in Deep Learning?2020-CVPR
- Additive Powers-of-Two Quantization: A Non-uniform Discretization for Neural Networks2020-ICLR
- Adversarial Neural Pruning with Latent Vulnerability Suppression2020-ICML
- APQ: Joint Search for Network Architecture, Pruning and Quantization Policy2020-CVPR
2018
- Accelerating Neural Architecture Search using Performance Prediction2018-ICLR
- Adaptive Mixture of Low-Rank Factorizations for Compact Neural Modeling2018-NIPS
- Adaptive Quantization of Neural Networks2018-ICLR
- A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication2018-NIPS
- Alternating Multi-bit Quantization for Recurrent Neural Networks2018-ICLR
- AMC: AutoML for Model Compression and Acceleration on Mobile Devices2018-ECCV
Showing a sample of 336 resources. View the full list on GitHub →