VITA-Group / triple-wins
[ICLR 2020] ”Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference“
☆24Updated 3 years ago
Alternatives and similar repositories for triple-wins:
Users that are interested in triple-wins are comparing it to the libraries listed below
- Codebase for the paper "A Gradient Flow Framework for Analyzing Network Pruning"☆21Updated 4 years ago
- [NeurIPS 2020] "Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free" by Haotao Wang*, Tianlong C…☆44Updated 3 years ago
- [NeurIPS'2019] Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, Ji Liu, “Model Compression with Adversarial Robustness: …☆49Updated 3 years ago
- [ICLR 2022] "Sparsity Winning Twice: Better Robust Generalization from More Efficient Training" by Tianlong Chen*, Zhenyu Zhang*, Pengjun…☆39Updated 3 years ago
- Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes☆23Updated 4 years ago
- Implementation of Effective Sparsification of Neural Networks with Global Sparsity Constraint☆31Updated 3 years ago
- Code and checkpoints of compressed networks for the paper titled "HYDRA: Pruning Adversarially Robust Neural Networks" (NeurIPS 2020) (ht…☆92Updated 2 years ago
- Experiments from "The Generalization-Stability Tradeoff in Neural Network Pruning": https://arxiv.org/abs/1906.03728.☆15Updated 4 years ago
- pytorch implementation of Parametric Noise Injection for adversarial defense☆44Updated 5 years ago
- Code for the Paper 'On the Connection Between Adversarial Robustness and Saliency Map Interpretability' by C. Etmann, S. Lunz, P. Maass, …☆16Updated 5 years ago
- Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection☆21Updated 4 years ago
- Codes for Understanding Architectures Learnt by Cell-based Neural Architecture Search☆27Updated 5 years ago
- Data-Free Network Quantization With Adversarial Knowledge Distillation PyTorch☆29Updated 3 years ago
- ☆19Updated 5 years ago
- ☆22Updated 5 years ago
- Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2…☆22Updated 4 years ago
- Adversarial Defense for Ensemble Models (ICML 2019)☆61Updated 4 years ago
- [JMLR] TRADES + random smoothing for certifiable robustness☆14Updated 4 years ago
- [ICLR 2021] "Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, S…☆25Updated 3 years ago
- [ICLR 2021 Spotlight Oral] "Undistillable: Making A Nasty Teacher That CANNOT teach students", Haoyu Ma, Tianlong Chen, Ting-Kuei Hu, Che…☆81Updated 3 years ago
- [ICML 2021] "Efficient Lottery Ticket Finding: Less Data is More" by Zhenyu Zhang*, Xuxi Chen*, Tianlong Chen*, Zhangyang Wang☆25Updated 3 years ago
- The Search for Sparse, Robustness Neural Networks☆11Updated 2 years ago
- [CVPR 2021] "The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models" Tianlong Chen, Jon…☆69Updated 2 years ago
- [NeurIPS‘2021] "MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge", Geng Yuan, Xiaolong Ma, Yanzhi Wang et al…☆18Updated 3 years ago
- Smooth Adversarial Training☆67Updated 4 years ago
- Code for Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot☆42Updated 4 years ago
- Paper and Code for "Curriculum Learning by Optimizing Learning Dynamics" (AISTATS 2021)☆19Updated 3 years ago
- Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search☆16Updated 8 months ago
- DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures☆32Updated 4 years ago
- ☆35Updated 4 years ago