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Model Compression

1674 directly classified papers

Papers per year

Papers

Complexity-Guided Slimmable Decoder for Efficient Deep Video Compression CVPR 2023
Global Vision Transformer Pruning With Hessian-Aware Saliency CVPR 2023
Fair Scratch Tickets: Finding Fair Sparse Networks Without Weight Training CVPR 2023
NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers CVPR 2023
SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient ICML 2023
Masked Autoencoders Enable Efficient Knowledge Distillers CVPR 2023
1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions CVPR 2023
Lowering the Pre-training Tax for Gradient-based Subset Training: A Lightweight Distributed Pre-Training Toolkit ICML 2023
X-Pruner: eXplainable Pruning for Vision Transformers CVPR 2023
BiBench: Benchmarking and Analyzing Network Binarization ICML 2023
Equivariant Architectures for Learning in Deep Weight Spaces ICML 2023
Outlier Suppression+: Accurate quantization of large language models by equivalent and effective shifting and scaling EMNLP 2023
Pit One Against Many: Leveraging Attention-head Embeddings for Parameter-efficient Multi-head Attention EMNLP 2023
Less Is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation CVPR 2023
Pruning Parameterization With Bi-Level Optimization for Efficient Semantic Segmentation on the Edge CVPR 2023
Compressing Volumetric Radiance Fields to 1 MB CVPR 2023
Learning To Retain While Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation CVPR 2023
STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition ICML 2023
Generic-to-Specific Distillation of Masked Autoencoders CVPR 2023
CP3: Channel Pruning Plug-In for Point-Based Networks CVPR 2023
Bit-Shrinking: Limiting Instantaneous Sharpness for Improving Post-Training Quantization CVPR 2023
Adaptive Data-Free Quantization CVPR 2023
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time ICML 2023
Boosting Accuracy and Robustness of Student Models via Adaptive Adversarial Distillation CVPR 2023
Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective CVPR 2023