Test-time Assessment of a Model's Performance on Unseen Domains via Optimal Transport

arXiv:2405.01451v1 Announce Type: new
Abstract: Gauging the performance of ML models on data from unseen domains at test-time is essential yet a challenging problem due to the lack of labels in this setting. Moreover, the performance of these models on in-distribution data is a poor indicator of their performance on data from unseen domains. Thus, it is essential to develop metrics that can provide insights into the model's performance at test time and can be computed only with the information …

abstract arxiv assessment cs.lg data distribution domains labels ml models performance s performance test transport type via