Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge

Belangrijkste concepten

Proposing a novel domain generalization framework, WDRDG, to address the challenge of limited labeled samples in source domains by leveraging Wasserstein distributional robust optimization.

Samenvatting

The content discusses the challenges of domain generalization and proposes a novel framework, WDRDG, to address these challenges. It introduces the concept of Wasserstein uncertainty sets and optimal transport for adaptive inference. The framework is evaluated on three datasets: VLCS, PACS, and Rotated MNIST, showing superior performance in handling unseen domain shifts with limited training data.

JOURNAL OF L AT EX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021

• Introduction to the concept of domain generalization and its challenges.
• Proposal of a novel domain generalization framework called WDRDG.
• Explanation of Wasserstein uncertainty sets and optimal transport for adaptive inference.
• Evaluation of the framework on VLCS, PACS, and Rotated MNIST datasets.

Data Extraction:

• "Experiments on the Rotated MNIST, PACS and the VLCS datasets demonstrate that our method could effectively balance the robustness and discriminability in challenging generalization scenarios."

Statistieken

Experiments on the Rotated MNIST, PACS and the VLCS datasets demonstrate that our method could effectively balance the robustness and discriminability in challenging generalization scenarios.

Citaten

"We propose a domain generalization framework that solves the Wasserstein distributionally robust optimization problem to learn a robust model over multiple source domains."
"Our main contributions include proposing a domain generalization framework that solves the Wasserstein distributionally robust optimization problem."

Belangrijkste Inzichten Gedestilleerd Uit

Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge

by Jingge Wang,... om arxiv.org 03-26-2024

https://arxiv.org/pdf/2207.04913.pdf

Diepere vragen

How does incorporating Wasserstein uncertainty sets improve model robustness

Incorporating Wasserstein uncertainty sets improves model robustness by explicitly modeling the unknown target domain shift under limited source knowledge. By defining class-specific uncertainty sets based on the Wasserstein distance, the framework encourages robustness over conditional distributions within these sets. This approach allows for a more explicit examination of distributional shifts among classes, enabling better management of varying degrees of domain perturbations for each class. The use of Wasserstein uncertainty sets ensures that the model is trained to be robust against potential perturbations in the data distribution, leading to improved generalization performance on unseen target domains.

What are potential limitations or drawbacks of using optimal transport for adaptive inference

One potential limitation or drawback of using optimal transport for adaptive inference is computational complexity. Optimal transport involves solving complex optimization problems that can be computationally intensive, especially when dealing with large datasets or high-dimensional feature spaces. This could result in increased processing time and resource requirements, making real-time applications challenging. Additionally, optimal transport methods may require tuning hyperparameters such as regularization parameters or distance metrics, which can add an additional layer of complexity to the implementation and optimization process.

How can this framework be applied to other domains beyond image classification

This framework can be applied to other domains beyond image classification by adapting it to different types of data and tasks while maintaining the core principles of Wasserstein distributionally robust optimization and test-time adaptation with optimal transport. For example:

Text Classification: Instead of image features, textual features could be used as inputs for classification tasks such as sentiment analysis or document categorization.
Healthcare Data: The framework could be applied to medical data for tasks like disease diagnosis or patient outcome prediction by leveraging patient records from multiple sources.
Financial Data: In finance, this framework could help in predicting market trends or identifying fraudulent activities by incorporating financial transaction data from various sources.
By customizing input representations and adjusting parameters according to specific domain characteristics, this framework can effectively generalize across diverse domains beyond just images.