Invariant Feature Learning for Generalized Long-Tailed Classification (2207.09504v2)

Abstract: Existing long-tailed classification (LT) methods only focus on tackling the class-wise imbalance that head classes have more samples than tail classes, but overlook the attribute-wise imbalance. In fact, even if the class is balanced, samples within each class may still be long-tailed due to the varying attributes. Note that the latter is fundamentally more ubiquitous and challenging than the former because attributes are not just implicit for most datasets, but also combinatorially complex, thus prohibitively expensive to be balanced. Therefore, we introduce a novel research problem: Generalized Long-Tailed classification (GLT), to jointly consider both kinds of imbalances. By "generalized", we mean that a GLT method should naturally solve the traditional LT, but not vice versa. Not surprisingly, we find that most class-wise LT methods degenerate in our proposed two benchmarks: ImageNet-GLT and MSCOCO-GLT. We argue that it is because they over-emphasize the adjustment of class distribution while neglecting to learn attribute-invariant features. To this end, we propose an Invariant Feature Learning (IFL) method as the first strong baseline for GLT. IFL first discovers environments with divergent intra-class distributions from the imperfect predictions and then learns invariant features across them. Promisingly, as an improved feature backbone, IFL boosts all the LT line-up: one/two-stage re-balance, augmentation, and ensemble. Codes and benchmarks are available on Github: https://github.com/KaihuaTang/Generalized-Long-Tailed-Benchmarks.pytorch

• The paper introduces the concept of Generalized Long-Tailed (GLT) classification, expanding the problem scope to include both class-wise and critical attribute-wise data imbalances.
• The proposed Invariant Feature Learning (IFL) framework learns features invariant to attribute variations within classes by standardizing attribute environments, mitigating bias from spurious correlations.
• New benchmarks (ImageNet-GLT, MSCOCO-GLT) show IFL significantly improves model performance, especially in the GLT context, highlighting the need for methods addressing attribute imbalance for robust models.

Invariant Feature Learning for Generalized Long-Tailed Classification

The paper "Invariant Feature Learning for Generalized Long-Tailed Classification" presents a novel approach to addressing the pervasive challenge of long-tailed data distributions in classification tasks. The authors argue that traditional long-tailed classification (LT) techniques often miss critical nuances by predominantly focusing on class-wise imbalances, thus sidelining the attribute-wise imbalances that can exist within each class. This oversight, the paper suggests, fails to account for the inherent complexity and ubiquity of attributes, which can significantly hinder a model's robustness.

New Contributions and Framework

This paper introduces the concept of Generalized Long-Tailed classification (GLT), expanding the scope of long-tailed classification by integrating both class-wise and attribute-wise imbalances into the problem formulation. By presenting GLT, the authors propose a paradigm where a solution must address both the conventional LT problems and the newly highlighted attribute-related issues — essentially extending beyond what previous methodologies have accomplished.

The cornerstone of their solution is the Invariant Feature Learning (IFL) framework. IFL is designed to combat attribute-wise imbalance by focusing on learning invariant features across diverse attribute environments within each class. The method operates by using a process that identifies and standardizes environments characterized by divergent intra-class distributions, thus learning features that remain consistent irrespective of these variations. This is expected to mitigate the bias introduced by spurious correlations between attributes and class predictions.

Benchmarks and Results

To evaluate their approach, the authors propose two benchmarks—ImageNet-GLT and MSCOCO-GLT—that challenge models with both types of imbalances, providing a comprehensive testing ground for this expanded classification task. The benchmarks are designed with unique protocols including Class-wise Long Tail (CLT) and Attribute-wise Long Tail (ALT) to dissect the improvements adequately. The results demonstrated that current LT methods struggle significantly under these generalized conditions, emphasizing the need for an approach like IFL.

The experiments show that IFL not only improves precision and accuracy but also elevates the performance of existing LT techniques when coupled with IFL as a new feature backbone. IFL demonstrates superiority particularly in the GLT context, outperforming traditional LT methods by successfully handling the additional complexity presented by attribute-wise imbalances.

Implications

The implications of this paper are profound for both theoretical growth and practical applications in AI. The introduction of GLT as a problem scope and the corresponding solution framework through IFL addresses a significant gap in existing classification methodologies—particularly in fields such as computer vision, where intra-class attribute diversity is commonplace. Furthermore, it implies a shift toward methods that prioritize robustness and consistency over purely accuracy-driven improvements.

Theoretically, it encourages a reconsideration of what "balanced" datasets mean, suggesting that considerations of balance must extend beyond mere class frequencies to the prevalence of attributes within those classes. Practically, this could lead to data collection and augmentation strategies that more accurately reflect real-world distributions, potentially improving model performance across diverse applications.

Future Work

The paper's trajectory opens avenues for future research to explore more sophisticated approaches in attribute disentanglement and invariance extraction, which are crucial for realizing robust and unbiased models. Additionally, it suggests the exploration of GLT and IFL concepts within other learning paradigms, such as domain adaptation and transfer learning, to further broaden their applicability and efficacy.

In conclusion, this paper marks a significant evolution in the treatment of long-tailed distributions, pushing the envelope beyond class frequency to encapsulate a holistic view of data imbalance. This shift is likely to inspire further advancements that establish more resilient and applicable AI models in complex data environments.