forgot password?
register here
Research articles
  search articles
  reviews guidelines
  articles index
My Pages
my alerts
  my messages
  my reviews
  my favorites
Members: 3652
Articles: 2'545'386
Articles rated: 2609

24 June 2024
  » arxiv » 2302.00194

 Article overview

Free Lunch for Domain Adversarial Training: Environment Label Smoothing
YiFan Zhang ; Xue Wang ; Jian Liang ; Zhang Zhang ; Liang Wang ; Rong Jin ; Tieniu Tan ;
Date 1 Feb 2023
AbstractA fundamental challenge for machine learning models is how to generalize learned models for out-of-distribution (OOD) data. Among various approaches, exploiting invariant features by Domain Adversarial Training (DAT) received widespread attention. Despite its success, we observe training instability from DAT, mostly due to over-confident domain discriminator and environment label noise. To address this issue, we proposed Environment Label Smoothing (ELS), which encourages the discriminator to output soft probability, which thus reduces the confidence of the discriminator and alleviates the impact of noisy environment labels. We demonstrate, both experimentally and theoretically, that ELS can improve training stability, local convergence, and robustness to noisy environment labels. By incorporating ELS with DAT methods, we are able to yield state-of-art results on a wide range of domain generalization/adaptation tasks, particularly when the environment labels are highly noisy.
Source arXiv, 2302.00194
Services Forum | Review | PDF | Favorites   
Visitor rating: did you like this article? no 1   2   3   4   5   yes

No review found.
 Did you like this article?

This article or document is ...
of broad interest:
Global appreciation:

  Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
» my Online CV
» Free

home  |  contact  |  terms of use  |  sitemap
Copyright © 2005-2024 - Scimetrica