Artificial intelligence and deep learning models, despite their popularity and capacity, often struggle with generalization, particularly when they encounter data that differs from what they were trained on. This issue arises when the distribution of training and testing data varies, resulting in reduced model performance.
The concept of domain generalization has been introduced to combat this problem. Domain generalization involves creating models that function effectively across different data distributions. However, building and comparing domain generalization methods have proven challenging because many of these methods are proof-of-concept rather than being modular and flexible for use with various datasets. This lack of modularity also impacts reproducibility and hinders an unbiased comparison of different techniques.
To rectify these problems, a research team has developed DomainLab, a modular Python package designed for domain generalization in deep learning. DomainLab aims to disentangle domain generalization components, allowing users to combine different algorithmic elements more comfortably. This modular approach enhances adaptability and simplifies the alteration of techniques to fit new use cases.
What sets DomainLab apart is its decoupled architecture, which keeps the regularisation loss construction separate from neural network development. As a result, users can define multiple domain generalization techniques, hierarchical combinations of neural networks, and related hyperparameters in a single configuration file.
DomainLab also allows users to modify individual model components without extensive code changes, promoting experimentation and increasing repeatability. Furthermore, the package provides robust benchmarking capabilities, allowing users to test the generalization performance of their neural networks on out-of-distribution data, whether on a single computer or a high-performance computing cluster.
The Python package focuses on dependability and usability, with an extensive test coverage of over 95%, ensuring reliable performance across various environments. Further, DomainLab includes comprehensive documentation, explaining every feature and its usage.
Maintaining the idea of being ‘closed to modification but open to extension,’ DomainLab offers core features that are stable yet permit users to add new features to customize the package to their specific needs. The package has been distributed under the permissive MIT license, giving users the flexibility to use, modify, and share the package as desired.
For more information, the research paper and the GitHub repository are available online. All credit for this research goes to the respective project researchers. Users are also encouraged to engage with the AI community via multiple online platforms, including Twitter, Telegram Channel, Discord Channel, and LinkedIn Group. A newsletter subscription service for interested users is also available.
