Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning

Autor:	Montesuma, Eduardo Fernandes, Mboula, Fred Ngolè, Souloumiac, Antoine
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	In this paper, we consider the intersection of two problems in machine learning: Multi-Source Domain Adaptation (MSDA) and Dataset Distillation (DD). On the one hand, the first considers adapting multiple heterogeneous labeled source domains to an unlabeled target domain. On the other hand, the second attacks the problem of synthesizing a small summary containing all the information about the datasets. We thus consider a new problem called MSDA-DD. To solve it, we adapt previous works in the MSDA literature, such as Wasserstein Barycenter Transport and Dataset Dictionary Learning, as well as DD method Distribution Matching. We thoroughly experiment with this novel problem on four benchmarks (Caltech-Office 10, Tennessee-Eastman Process, Continuous Stirred Tank Reactor, and Case Western Reserve University), where we show that, even with as little as 1 sample per class, one achieves state-of-the-art adaptation performance. Comment: 7 pages,4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.07666 Zobrazit plný text záznamu View this record from Arxiv