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DomainLab: A modular Python package for domain generalization in deep learning by Sun, Xudong; Feistner, Carla; Gossmann, Alexej; Schwarz, George; Umer, Rao Muhammad; Beer, Lisa; Rockenschaub, Patrick; Shrestha, Rahul Babu; Gruber, Armin; Chen, Nutan; Boushehri, Sayedali Shetab; Buettner, Florian; Marr, Carsten is a scholarly article available to read on EtoBox.

What is DomainLab: A modular Python package for domain generalization in deep learning about?

Poor generalization performance caused by distribution shifts in unseen domains often hinders the trustworthy deployment of deep neural networks. Many domain generalization techniques address this problem by adding a domain invariant regularization loss terms during training. However, there is a lack of modular software that allows users to combine the advantages of different methods with minimal effort for reproducibility. DomainLab is a modular Python package for training user specified neural networks with composable regularization loss terms. Its decoupled design allows the separation of neural networks from regularization loss construction. Hierarchical combinations of neural networks, different domain generalization methods, and associated hyperparameters, can all be specified together with other experimental setup in a single configuration file. Hierarchical combinations of neural networks, different domain generalization methods, and associated hyperparameters, can all be specified together with other experimental setup in a single configuration file. In addition, DomainLab offers powerful benchmarking functionality to evaluate the generalization performance of neural netwo

Author

Sun, Xudong; Feistner, Carla; Gossmann, Alexej; Schwarz, George; Umer, Rao Muhammad; Beer, Lisa; Rockenschaub, Patrick; Shrestha, Rahul Babu; Gruber, Armin; Chen, Nutan; Boushehri, Sayedali Shetab; Buettner, Florian; Marr, Carsten

Published

2024

Language

EN