DomainLab: A Modular Python Package for Domain Generalization in Deep Learning

Deep learning models have recently gained significant popularity in the Artificial Intelligence community. However, despite their great capacity, they frequently suffer from poor generalization. This implies that when they encounter data that is different from what they were trained on, their performance suffers noticeably. The performance of the model is negatively impacted when the distribution of the data used for training and testing differs. 

Researchers have come up with domain generalization to overcome this problem by developing models that function effectively across various data distributions. However, it has been difficult to construct and compare domain generalization techniques. Rather than being solid, modular software, many of the current implementations are more in the stage of proof-of-concept code. They are less flexible when it comes to using different datasets since they frequently include custom code for operations like data access, preparation, and evaluation. This lack of modularity impairs reproducibility and makes it challenging to conduct an unbiased comparison of various approaches.

In order to address these challenges, a team of researchers has introduced DomainLab, a modular Python package for domain generalization in deep learning. This python package seeks to disentangle the elements of domain generalization techniques so that users can more readily mix various algorithmic components. This modular strategy improves adaptability and streamlines the process of changing techniques to suit new use cases. 

DomainLab is a modular Python package with adjustable regularisation loss terms made especially for neural network training. It is unique because of its decoupled architecture, which keeps the regularisation loss construction and neural network development separate. With this design decision, users can specify multiple domain generalization techniques, hierarchical combinations of neural networks, and related hyperparameters in a single configuration file.

The team has shared that users can readily modify individual model components without significant code changes, which facilitates experimentation and promotes repeatability. DomainLab also offers robust benchmarking capabilities that let users assess their neural networks’ generalization performance on out-of-distribution data. Depending on the user’s resources, the benchmarking might be done on a solo computer or on a cluster of high-performance computers (HPCs). 

Dependability and usability are key design considerations in DomainLab. With more than 95% coverage, its extensive testing guarantees that the package performs as intended in a variety of settings. Additionally, the package comes with extensive documentation that explains all of the features and how to utilize them.

The team has shared that from the user’s point of view, DomainLab adheres to the idea of being ‘closed to modification but open to extension,’ which means that although the core features are solid and well-defined, users can add new features to customize it to their own requirements. In addition, the package has been distributed under the permissive MIT license, which gives users the flexibility to use, alter, and share it as they see fit. 

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