Model Compression

Against Membership Inference Attack: Pruning is All You Need IJCAI 2021 An Efficient Transformer Decoder with Compressed Sub-layers AAAI 2021 Any-Precision Deep Neural Networks AAAI 2021 Anytime Inference with Distilled Hierarchical Neural Ensembles AAAI 2021 AQD: Towards Accurate Quantized Object Detection CVPR 2021 A Unified Lottery Ticket Hypothesis for Graph Neural Networks ICML 2021 AutoLR: Layer-wise Pruning and Auto-tuning of Learning Rates in Fine-tuning of Deep Networks AAAI 2021 Automated Log-Scale Quantization for Low-Cost Deep Neural Networks CVPR 2021 BASE Layers: Simplifying Training of Large, Sparse Models ICML 2021 Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression CVPR 2021 BCNet: Searching for Network Width With Bilaterally Coupled Network CVPR 2021 Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression EMNLP 2021 Bi-GCN: Binary Graph Convolutional Network CVPR 2021 BinaryBERT: Pushing the Limit of BERT Quantization ACL 2021 Binary Graph Neural Networks CVPR 2021 Bit-Mixer: Mixed-Precision Networks With Runtime Bit-Width Selection ICCV 2021 Block Pruning For Faster Transformers EMNLP 2021 Block-wise Word Embedding Compression Revisited: Better Weighting and Structuring EMNLP 2021 CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks AAAI 2021 CDFI: Compression-Driven Network Design for Frame Interpolation CVPR 2021 Channel Permutations for N:M Sparsity NeurIPS 2021 Chasing Sparsity in Vision Transformers: An End-to-End Exploration NeurIPS 2021 CHIP: CHannel Independence-based Pruning for Compact Neural Networks NeurIPS 2021 ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic CVPR 2021 Compatibility-Aware Heterogeneous Visual Search CVPR 2021