szkocot / Adapting-Auxiliary-Losses-Using-Gradient-SimilarityLinks
Implementation "Adapting Auxiliary Losses Using Gradient Similarity" article
☆32Updated 6 years ago
Alternatives and similar repositories for Adapting-Auxiliary-Losses-Using-Gradient-Similarity
Users that are interested in Adapting-Auxiliary-Losses-Using-Gradient-Similarity are comparing it to the libraries listed below
Sorting:
- Code for the paper "Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets", ICCV 2019☆27Updated 5 years ago
- Paper and Code for "Curriculum Learning by Optimizing Learning Dynamics" (AISTATS 2021)☆19Updated 4 years ago
- Code release for Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning (NeurIPS 2019)☆24Updated 3 years ago
- NeurIPS'18: Masking: A New Perspective of Noisy Supervision☆54Updated 6 years ago
- ☆28Updated 3 years ago
- This is the code of CVPR'20 paper "Distilling Cross-Task Knowledge via Relationship Matching".☆49Updated 4 years ago
- [CVPR 2020] Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning☆85Updated 3 years ago
- Code release for "Transferable Normalization: Towards Improving Transferability of Deep Neural Networks" (NeurIPS 2019)☆79Updated 4 years ago
- [ICLR 2021 Spotlight Oral] "Undistillable: Making A Nasty Teacher That CANNOT teach students", Haoyu Ma, Tianlong Chen, Ting-Kuei Hu, Che…☆82Updated 3 years ago
- [NeurIPS 2021] “Improving Contrastive Learning on Imbalanced Data via Open-World Sampling”, Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangya…☆28Updated 3 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
- PyTorch implementation of Weighted Batch-Normalization layers☆37Updated 5 years ago
- [CVPR 2021] "The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models" Tianlong Chen, Jon…☆68Updated 2 years ago
- Self-supervised Label Augmentation via Input Transformations (ICML 2020)☆107Updated 4 years ago
- ☆23Updated 6 years ago
- ICML'20: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust☆17Updated 4 years ago
- Improving Contrastive Learning by Visualizing Feature Transformation, ICCV 2021 Oral☆90Updated 4 years ago
- Code for CVPR2021 paper: MOOD: Multi-level Out-of-distribution Detection☆38Updated 2 years ago
- ☆21Updated 6 years ago
- Automatic model evaluation (AutoEval) in CVPR'21&TPAMI'22☆37Updated 3 years ago
- [ICLR 2021] Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization☆41Updated 4 years ago
- Q. Yao, H. Yang, B. Han, G. Niu, J. Kwok. Searching to Exploit Memorization Effect in Learning from Noisy Labels. ICML 2020☆23Updated 5 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆89Updated 6 years ago
- [ICML 2021] “ Self-Damaging Contrastive Learning”, Ziyu Jiang, Tianlong Chen, Bobak Mortazavi, Zhangyang Wang☆63Updated 3 years ago
- Code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning", Ren et al., NeurIPS'20☆25Updated 4 years ago
- AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning☆114Updated 4 years ago
- Code for 'Joint Optimization Framework for Learning with Noisy Labels'☆38Updated 7 years ago
- Learning recognition/segmentation models without end-to-end training. 40%-60% less GPU memory footprint. Same training time. Better perfo…☆90Updated 3 years ago
- Full implementation of the paper "Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimator".☆101Updated 5 years ago
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Updated 4 years ago