Interpretable Domain Adaptation for Hidden Subdomain Alignment in the Context of Pre-Trained Source Models

Luxin ZHANG, Pascal Germain, Yacine Kessaci, Christophe Biernacki

[AAAI-22] Main Track
Abstract: Domain adaptation aims to leverage source domain knowledge to predict target domain labels. Most domain adaptation methods tackle a single-source, single-target scenario, whereas source and target domain data can often be subdivided into data from different distributions in real-life applications (e.g., when the distribution of the collected data changes with time). However, such subdomains are rarely given and should be discovered automatically.

To this end, some recent domain adaptation works seek separations of hidden subdomains, w.r.t. a known or fixed number of subdomains.

In contrast, this paper introduces a new subdomain combination method that leverages a variable number of subdomains.

Precisely, we propose to use an inter-subdomain divergence maximization criterion to exploit hidden subdomains.

Besides, our proposition stands in a target-to-source domain adaptation scenario, where one exploits a pre-trained source model as a black box; thus, the proposed method is model-agnostic.

By providing interpretability at two complementary levels (transformation and subdomain levels), our method can also be easily interpreted by practitioners with or without machine learning backgrounds.

Experimental results over two fraud detection datasets demonstrate the efficiency of our method.

Introduction Video

Sessions where this paper appears

  • Poster Session 1

    Blue 3

  • Poster Session 12

    Blue 3