Summary of Domainlab: a Modular Python Package For Domain Generalization in Deep Learning, by Xudong Sun et al.
DomainLab: A modular Python package for domain generalization in deep learning
by Xudong Sun, Carla Feistner, Alexej Gossmann, George Schwarz, Rao Muhammad Umer, Lisa Beer, Patrick Rockenschaub, Rahul Babu Shrestha, Armin Gruber, Nutan Chen, Sayedali Shetab Boushehri, Florian Buettner, Carsten Marr
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Software Engineering (cs.SE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the issue of poor generalization performance in deep neural networks due to distribution shifts in unseen domains. The authors propose DomainLab, a modular Python package that allows users to combine different domain generalization methods with minimal effort for reproducibility. DomainLab decouples the design of neural networks from regularization loss construction, enabling hierarchical combinations of neural networks, domain generalization methods, and hyperparameters to be specified together in a single configuration file. The package also includes powerful benchmarking functionality to evaluate the generalization performance of neural networks on out-of-distribution data. The authors report that DomainLab is well tested with over 95% coverage and well documented. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DomainLab is a new software tool that helps fix a big problem in artificial intelligence called domain shift. Imagine you train a computer program to recognize cats, but it doesn’t work very well when you show it pictures of dogs. This happens because the training data didn’t have any dog pictures. DomainLab lets you mix and match different techniques to help your AI model generalize better. You can even test how well it does on new, unseen data. The tool is easy to use and comes with lots of examples to get started. |
Keywords
* Artificial intelligence * Domain generalization * Generalization * Regularization
