On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources

11/27/2021
by   Trung Phung, et al.
0

Domain adaptation (DA) benefits from the rigorous theoretical works that study its insightful characteristics and various aspects, e.g., learning domain-invariant representations and its trade-off. However, it seems not the case for the multiple source DA and domain generalization (DG) settings which are remarkably more complicated and sophisticated due to the involvement of multiple source domains and potential unavailability of target domain during training. In this paper, we develop novel upper-bounds for the target general loss which appeal to us to define two kinds of domain-invariant representations. We further study the pros and cons as well as the trade-offs of enforcing learning each domain-invariant representation. Finally, we conduct experiments to inspect the trade-off of these representations for offering practical hints regarding how to use them in practice and explore other interesting properties of our developed theory.

READ FULL TEXT
research
10/15/2020

Improved Multi-Source Domain Adaptation by Preservation of Factors

Domain Adaptation (DA) is a highly relevant research topic when it comes...
research
10/09/2020

Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation

The success of supervised learning hinges on the assumption that the tra...
research
07/08/2019

Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks

(Unsupervised) Domain Adaptation (DA) seeks for classifying target insta...
research
06/09/2021

Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization

Domain shifts in the training data are common in practical applications ...
research
08/27/2019

Domain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation

Domain Adaptation (DA) has the potential to greatly help the generalizat...
research
11/30/2020

Heuristic Domain Adaptation

In visual domain adaptation (DA), separating the domain-specific charact...
research
06/20/2020

Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations

We investigate the power of censoring techniques, first developed for le...

Please sign up or login with your details

Forgot password? Click here to reset