dengbaowang / Mixup-Inference-in-TrainingLinks
This is the implementation of our CVPR'23 paper On the Pitfall of Mixup for Uncertainty Calibration. In the paper, we conduct a series of empirical studies showing the calibration issue of Mixup, and propose a new mixup training strategy to address this issue.
☆17Updated 2 years ago
Alternatives and similar repositories for Mixup-Inference-in-Training
Users that are interested in Mixup-Inference-in-Training are comparing it to the libraries listed below
Sorting:
- PyTorch implementation of CIDER (How to exploit hyperspherical embeddings for out-of-distribution detection), ICLR 2023☆62Updated 2 years ago
- [ICML 2023] On Pitfalls of Test-Time Adaptation☆121Updated last year
- ☆15Updated 3 years ago
- A list of papers that studies out-of-distribution (OOD) detection and misclassification detection (MisD)☆49Updated last year
- The offical implement of ImbSAM (Imbalanced-SAM)☆24Updated last year
- Official Pytorch implementation of "Learning Debiased Representation via Disentangled Feature Augmentation (Neurips 2021, Oral)"☆103Updated 2 years ago
- [NeurIPS 2023] "Learning to Augment Distributions for Out-of-distribution Detection"☆12Updated last year
- [NeurIPS 2022] The official code for our NeurIPS 2022 paper "Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnab…☆45Updated 2 years ago
- Official PyTorch implementation of "Loss-Curvature Matching for Dataset Selection and Condensation" (AISTATS 2023)☆21Updated 2 years ago
- PyTorch implementation of our CVPR2023 paper "OpenMix: Exploring Out-of-Distribution samples for Misclassification Detection"☆25Updated last year
- [WACV'23] Mixture Outlier Exposure for Out-of-Distribution Detection in Fine-grained Environments☆27Updated 2 years ago
- Code for Model Agnostic Sample Reweighting for Out-of-Distribution Learning☆44Updated 2 years ago
- This is an official PyTorch implementation of the ICML 2023 paper AdaNPC and SIGKDD paper DRM.☆86Updated last year
- NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"☆39Updated 3 years ago
- Code for ICML 2022 paper — Efficient Test-Time Model Adaptation without Forgetting☆128Updated 2 years ago
- PyTorch implementation of our Adam-NSCL algorithm from our CVPR2021 (oral) paper "Training Networks in Null Space for Continual Learning"☆64Updated 4 years ago
- ☆26Updated 2 years ago
- Self-Supervised Dataset Distillation for Transfer Learning☆16Updated last year
- ☆23Updated last year
- ☆18Updated 2 years ago
- ☆31Updated 4 months ago
- Official implementation for "Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition" (I…☆43Updated 2 years ago
- Code for "Surgical Fine-Tuning Improves Adaptation to Distribution Shifts" published at ICLR 2023☆29Updated 2 years ago
- This is the official PyTorch Implementation of "SoTTA: Robust Test-Time Adaptation on Noisy Data Streams (NeurIPS '23)" by Taesik Gong*, …☆22Updated last year
- CVPR 2022: Selective-Supervised Contrastive Learning with Noisy Labels☆95Updated 3 years ago
- Official Implementation of Robust Training under Label Noise by Over-parameterization☆64Updated 3 years ago
- The official codes of our CVPR-2023 paper: Sharpness-Aware Gradient Matching for Domain Generalization☆76Updated 2 years ago
- Code for NeurIPS 2021 paper "ReAct: Out-of-distribution Detection With Rectified Activations"☆55Updated 3 years ago
- ☆30Updated 2 years ago
- The Official Repository for CVPR2023 Paper "NICO++: Towards Better Benchmarking for Domain Generalization".☆41Updated 2 years ago