Summary of Dataset Dictionary Learning in a Wasserstein Space For Federated Domain Adaptation, by Eduardo Fernandes Montesuma et al.
Dataset Dictionary Learning in a Wasserstein Space for Federated Domain Adaptation
by Eduardo Fernandes Montesuma, Fabiola Espinoza Castellon, Fred Ngolè Mboula, Aurélien Mayoue, Antoine Souloumiac, Cédric Gouy-Pailler
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper proposes a novel approach to Multi-Source Domain Adaptation (MSDA) that addresses the challenge of adapting multiple related and heterogeneous source datasets to an unlabeled target dataset while preserving data privacy. The proposed method, Decentralized Dataset Dictionary Learning, leverages Wasserstein barycenters to model the distributional shift across clients, enabling effective adaptation without centralizing client data. The algorithm expresses each client’s underlying distribution as a Wasserstein barycenter of public atoms, weighted by private barycentric coordinates that remain undisclosed throughout the adaptation process. Experimental results on five visual domain adaptation benchmarks demonstrate the superiority of this approach over existing decentralized MSDA techniques, while also exhibiting enhanced robustness to client parallelism and relative resilience compared to conventional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine you have many different images from different sources, like cameras or phones, but they’re all related in some way. The goal is to teach a computer how to recognize things in these images, even if the images are very different from each other. But what if someone doesn’t want to share their images because it’s private information? This paper solves this problem by finding a way to adapt many different images at once without sharing any individual images. It uses a special math technique called Wasserstein barycenters to help the computer learn how to recognize things in all these images, while keeping each image private. The results show that this approach is better than other methods for doing this kind of adaptation. |
Keywords
* Artificial intelligence * Domain adaptation
