Implicit Task-Driven Probability Discrepancy Measure for Unsupervised Domain Adaptation.
Year: 2021
Type: article
Abstract: Probability discrepancy measure is a fundamental construct for numerous machine learning models such as weakly supervised learning and generative modeling. However, most measures overlook the fact that the distributions are not the end-product of learning, but are the input of a downstream predictor. Therefore, it is important to warp the probability discrepancy measure towards the end tasks, and towards this goal, we propose a new bi-level optimization based approach so that the two distributions are compared not uniformly against the entire hypothesis space, but only with respect to the optimal predictor for the downstream end task. When applied to margin disparity discrepancy and contrastive domain discrepancy, our method significantly improves the performance in unsupervised domain adaptation, and enjoys a much more principled training process.
Source: PubMed
Authors: Mao Li, Kaiqi Jiang, Xinhua Zhang
Institution: University of Illinois Chicago
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Cancer Institute
OpenAlex ID: W3214249198