zhuohuangai / HOOD
Code for ICLR 2023 Harnessing Out-Of-Distribution Examples via Augmenting Content and Style
☆13Updated last year
Alternatives and similar repositories for HOOD:
Users that are interested in HOOD are comparing it to the libraries listed below
- Code for CVPR 2023 Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization☆13Updated last year
- Source code for the NeurIPS 2023 paper: "CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels"☆17Updated last year
- PyTorch implementation of POEM (Out-of-distribution detection with posterior sampling), ICML 2022☆28Updated last year
- ☆25Updated last year
- ☆10Updated last year
- Test-Time Adaptation via Conjugate Pseudo-Labels☆39Updated last year
- ☆24Updated last year
- This is the official reporsitory for paper Causal Balancing for Domain Generalization☆12Updated last year
- ☆10Updated last year
- ☆16Updated 8 months ago
- Official code base for "Long-Tailed Diffusion Models With Oriented Calibration" ICLR2024☆11Updated 6 months ago
- [CVPR23] "Understanding and Improving Visual Prompting: A Label-Mapping Perspective" by Aochuan Chen, Yuguang Yao, Pin-Yu Chen, Yihua Zha…☆52Updated last year
- translation of VHL repo in paddle☆25Updated last year
- [NeurIPS 2022] The official code for our NeurIPS 2022 paper "Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnab…☆44Updated 2 years ago
- This repo implements the CVPR23 paper Trainable Projected Gradient Method for Robust Fine-tuning☆24Updated last year
- [ICML'24] Open-Vocabulary Calibration for Fine-tuned CLIP☆12Updated 7 months ago
- [ECCV 2022] "Adversarial Contrastive Learning via Asymmetric InfoNCE"☆22Updated 2 years ago
- [ICML 2023] "Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability"☆18Updated last year
- Official implementation of "Towards Distribution-Agnostic Generalized Category Discovery" (NIPS 2023)☆24Updated last year
- ☆17Updated 8 months ago
- The implementation for ICLR2023 paper: "BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion" in PyTorch.☆14Updated last year
- Implementation of Concept-level Debugging of Part-Prototype Networks☆11Updated last year