MAP: Towards Balanced Generalization of IID and OOD through Model-Agnostic Adapters-Reference-Cited by-同舟云学术

MAP: Towards Balanced Generalization of IID and OOD through Model-Agnostic Adapters

Published:2023-10-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
Short-container-title:

Author:

Zhang Min¹,Yuan Junkun¹,He Yue²,Li Wenbin³,Chen Zhengyu¹,Kuang Kun¹

Affiliation:

1. Zhejiang University

2. Tsinghua University

3. Nanjing University

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Institute for Advanced Study

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10376503.pdf?arnumber=10376503

Reference64 articles.

1. Systematic generalisation with group invariant predictions;Ahmed

2. Invariance principle meets information bottleneck for out-of-distribution generalization;Ahuja;Advances in Neural Information Processing Systems, NeurIPS,2021

3. Domain generalization by marginal transfer learning;Blanchard;The Journal of Machine Learning Research JMLR,2021

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Neural Collapse Anchored Prompt Tuning for Generalizable Vision-Language Models;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

3. Uncovering the Propensity Identification Problem in Debiased Recommendations;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13