On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond (2203.09513v3)

Abstract: Real-world data often exhibit imbalanced label distributions. Existing studies on data imbalance focus on single-domain settings, i.e., samples are from the same data distribution. However, natural data can originate from distinct domains, where a minority class in one domain could have abundant instances from other domains. We formalize the task of Multi-Domain Long-Tailed Recognition (MDLT), which learns from multi-domain imbalanced data, addresses label imbalance, domain shift, and divergent label distributions across domains, and generalizes to all domain-class pairs. We first develop the domain-class transferability graph, and show that such transferability governs the success of learning in MDLT. We then propose BoDA, a theoretically grounded learning strategy that tracks the upper bound of transferability statistics, and ensures balanced alignment and calibration across imbalanced domain-class distributions. We curate five MDLT benchmarks based on widely-used multi-domain datasets, and compare BoDA to twenty algorithms that span different learning strategies. Extensive and rigorous experiments verify the superior performance of BoDA. Further, as a byproduct, BoDA establishes new state-of-the-art on Domain Generalization benchmarks, highlighting the importance of addressing data imbalance across domains, which can be crucial for improving generalization to unseen domains. Code and data are available at: https://github.com/YyzHarry/multi-domain-imbalance.

• The paper introduces BoDA, a novel method that leverages domain-class transferability graphs to align and balance data distributions across multiple domains.
• Experiments on five MDLT benchmarks show that BoDA outperforms twenty competitive methods, significantly improving recognition for minority classes.
• The proposed approach is applicable to real-world tasks like visual recognition, autonomous driving, and medical diagnosis, enhancing robustness in diverse domains.

Essay on "On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization, and Beyond"

The paper, authored by Yuzhe Yang et al., addresses the challenges associated with real-world data characterized by imbalanced label distributions across multiple domains, which is central to the problem of Multi-Domain Long-Tailed Recognition (MDLT). MDLT presents unique challenges arising from domain shifts and varying label distributions across these domains, posing a significant challenge to the generalization capabilities of recognition models. The authors introduce the concept of a domain-class transferability graph to better understand and model these complexities, and they propose BoDA (Balanced Domain-Class Distribution Alignment), a novel theoretically grounded learning strategy aimed at balancing and aligning these imbalances effectively across domain-class distributions.

Key contributions of this work include formalizing the MDLT task and designing the domain-class transferability graph, which quantifies the transferability between domain-class pairs under data imbalance. This approach highlights that effective learning in MDLT depends on how well the transferability is managed across domains. The BoDA learning strategy intelligently balances the alignment of features by minimizing a loss function that upper bounds these transferability statistics, ensuring features are transferable across domains while accounting for class imbalance. Notably, this strategy not only enhances in-domain recognition but also sets new standards for Domain Generalization (DG), showcasing how addressing cross-domain data imbalance elevates performance in unseen domains.

The paper demonstrates the superiority of BoDA through extensive experiments across five curated MDLT benchmarks, employing various datasets frequently used in DG contexts. BoDA consistently outperforms twenty existing methods, ranging from standard empirical risk minimization (ERM) approaches to sophisticated domain-invariant representation learning strategies. The authors substantiate these results by presenting detailed analyses highlighting how BoDA improves feature alignment and reduces distributional divergences effectively, particularly for minority classes.

The implications of this research are substantial both theoretically and practically. Theoretically, it opens new avenues for understanding and addressing multi-domain data imbalance through rigorous transferability models and loss functions. Practically, the solutions offered by BoDA can be directly applied to real-world applications where data comes from multiple sources, such as in visual recognition tasks, autonomous driving, and medical diagnosis, improving the robustness and accuracy of AI systems faced with such complex multi-domain imbalances.

The paper positions BoDA as an important milestone, with the potential for its application extending beyond MDLT to enhance generalization capabilities in DG settings. As AI continues to evolve, the methodologies outlined here could become vital for developing systems that are not only accurate but also equitable in handling rare events and underrepresented classes across diverse domains.

Future work could further explore the integration of BoDA with other domain generalization techniques. Additionally, extending the adaptability of BoDA in dynamic environments where domain characteristics constantly evolve could further enhance its utility across broader AI tasks. The insights and methods proposed by Yang et al. provide a robust framework for tackling the ongoing challenges of data imbalance and domain diversity, setting a solid foundation for further research and development in multi-domain learning paradigms.