Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective (2003.10780v1)

Abstract: Object frequency in the real world often follows a power law, leading to a mismatch between datasets with long-tailed class distributions seen by a machine learning model and our expectation of the model to perform well on all classes. We analyze this mismatch from a domain adaptation point of view. First of all, we connect existing class-balanced methods for long-tailed classification to target shift, a well-studied scenario in domain adaptation. The connection reveals that these methods implicitly assume that the training data and test data share the same class-conditioned distribution, which does not hold in general and especially for the tail classes. While a head class could contain abundant and diverse training examples that well represent the expected data at inference time, the tail classes are often short of representative training data. To this end, we propose to augment the classic class-balanced learning by explicitly estimating the differences between the class-conditioned distributions with a meta-learning approach. We validate our approach with six benchmark datasets and three loss functions.

Analysis of Class-Balanced Methods in Long-Tailed Visual Recognition from a Domain Adaptation Viewpoint

The paper "Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective" introduces an analytical framework for addressing the challenges posed by long-tailed distributions in visual recognition tasks. This research primarily critiques conventional class-balanced methods and proposes a novel approach to tackle the intrinsic imbalance observed between training and inference phases in this context.

Introduction and Problem Formulation

The issue of long-tailed distributions is prevalent in various real-world datasets, where a few classes (head) encompass the majority of samples, while many classes (tail) are scarcely represented. This imbalance leads to biased learning models that underperform on minority classes. Existing solutions often adapt class-balanced loss mechanisms, assuming that discrepancies can be mitigated solely through adjusting class weights based on class frequency. This work challenges that assumption by examining it through a domain adaptation lens, highlighting that the conditional distribution mismatch between training (source) and inference (target) data is not adequately addressed by these methods.

Methodology

The authors draw parallels with the domain adaptation setting, suggesting that the learning process in long-tailed visual recognition should consider potential differences in class-conditional distributions. They present a meta-learning approach to alleviate this discrepancy, an improvement over traditional class-balanced methods.

1. Domain Adaptation Insight: The paper equates long-tail recognition to target shift in domain adaptation, asserting that $P_s(x|y) \neq P_t(x|y)$ may hold, particularly for tail classes where data scarcity limits representativity.
2. Two-Component Weighting Scheme: The core proposition involves extending class-balanced learning by integrating meta-learned conditional weights ($\epsilon_{x,y}$) alongside traditional class weights ($w_y$), facilitating adjustments beyond mere class frequency normalization.
3. Meta-Learning Implementation: The proposed approach uses a balanced development set to dynamically adjust the importance of training samples during learning. This involves a two-component weight for each training instance, capturing both class-level and conditional discrepancies.

Empirical Validation

The approach is empirically validated across multiple datasets, including long-tailed versions of CIFAR-10, CIFAR-100, ImageNet-LT, and naturally long-tailed datasets like iNaturalist. The evaluation demonstrates significant improvement in classification accuracy, particularly for tail classes, compared to conventional class-balanced and other meta-learning strategies. High imbalance scenarios see the most pronounced benefits, underscoring the efficacy of addressing conditional distribution shifts.

Implications and Future Directions

This research has implications for the development of robust models in settings with imbalanced data. By providing a framework that dissects and addresses multiple components of distributional mismatch, it expands the toolkit available for handling imbalanced classification beyond simple frequency-based adjustments. The work also suggests future exploration into other domain adaptation mechanisms and their potential to further enhance long-tailed visual recognition.

Conclusion

In summary, this paper contributes significantly to the understanding and methodology of handling long-tailed distribution challenges in visual recognition. The proposed meta-learning augmentation, which considers both class-balanced and conditional weighting, advances the efficacy of recognition models in real-world applications with inherent class imbalances. Future research could potentially extend this framework to encompass other adaptive learning strategies observed in domain adaptation literature to further optimize performance across diverse datasets and tasks.