Long-Tailed Recognition via Weight Balancing (2203.14197v1)

Abstract: In the real open world, data tends to follow long-tailed class distributions, motivating the well-studied long-tailed recognition (LTR) problem. Naive training produces models that are biased toward common classes in terms of higher accuracy. The key to addressing LTR is to balance various aspects including data distribution, training losses, and gradients in learning. We explore an orthogonal direction, weight balancing, motivated by the empirical observation that the naively trained classifier has "artificially" larger weights in norm for common classes (because there exists abundant data to train them, unlike the rare classes). We investigate three techniques to balance weights, L2-normalization, weight decay, and MaxNorm. We first point out that L2-normalization "perfectly" balances per-class weights to be unit norm, but such a hard constraint might prevent classes from learning better classifiers. In contrast, weight decay penalizes larger weights more heavily and so learns small balanced weights; the MaxNorm constraint encourages growing small weights within a norm ball but caps all the weights by the radius. Our extensive study shows that both help learn balanced weights and greatly improve the LTR accuracy. Surprisingly, weight decay, although underexplored in LTR, significantly improves over prior work. Therefore, we adopt a two-stage training paradigm and propose a simple approach to LTR: (1) learning features using the cross-entropy loss by tuning weight decay, and (2) learning classifiers using class-balanced loss by tuning weight decay and MaxNorm. Our approach achieves the state-of-the-art accuracy on five standard benchmarks, serving as a future baseline for long-tailed recognition.

An Examination of Long-Tailed Recognition Through Weight Balancing

The paper "Long-Tailed Recognition via Weight Balancing," authored by Shaden Alshammari et al., presents a detailed exploration of techniques to enhance the performance of deep learning models when faced with long-tailed class distributions. This research addresses a ubiquitous challenge in real-world datasets where a few classes are overly represented with abundant data, while numerous other classes have scant data. The paper proposes the incorporation of weight balancing strategies as an orthogonal approach to achieving more equitable classification accuracy across all classes.

Introduction and Motivation

In typical scenarios involving long-tailed datasets, naive training paradigms tend to bias model performance towards the more frequent classes, consequentially relegating rare classes to inaccurate predictions. The paper identifies that a key factor contributing to this imbalance is the larger weight norms for frequent classes, a direct byproduct of having more substantial data for learning in comparison to rare classes. Prior work predominantly focused on balancing data distributions or re-weighting loss functions. However, the authors propose balancing network weights, exploiting regularization techniques seemingly underexplored in the context of long-tailed recognition.

Methodology and Techniques

The paper evaluates three primary weight balancing techniques: L2-normalization, weight decay, and MaxNorm constraints.

• L2-Normalization: This technique enforces unit norms across all classifier weights, ensuring balanced magnitudes irrespective of data abundance. However, the paper notes that while L2-normalization constrains growth, it may also impede optimal learning by overly restricting parameter flexibility.
• Weight Decay: Leveraging the L2-norm penalty as a regularizer, weight decay encourages smaller, balanced weights by penalizing large norms more heavily. The authors highlight that, although weight decay is a classical method, it has not been significantly explored for long-tailed distribution challenges.
• MaxNorm Constraint: Functioning by capping weight norms within a specific radius, MaxNorm provides a more flexible constraint than L2-normalization, promoting the growth of smaller weights within a norm ball.

The paper suggests utilizing these techniques within a two-stage training paradigm: the first stage focuses on feature learning with weight decay, while the second stage refines the classifier using class-balanced loss with or without MaxNorm constraints.

Experimental Validation

The researchers validate their proposed framework across five benchmark datasets with varying degrees of imbalance, including CIFAR100-LT, ImageNet-LT, and iNaturalist2018. Notable improvements over existing methods, including those employing complex ensembling or data augmentation, are reported with the simpler weight balancing techniques.

On CIFAR100-LT with an imbalance factor of 100, they achieve an accuracy of 53.35%, considerably surpassing earlier methods such as RIDE and PaCo. The application of MaxNorm, combined with weight decay and class-balanced losses, not only enhances performance but also ensures weight norms are balanced even in challenging imbalanced settings.

Discussion and Implications

The findings underscore that precise weight regulation, a traditionally classical notion in deep learning, offers substantial gains for long-tailed recognition tasks. The experiments advocate a reconsideration of training pipelines to prioritize weight balancing, especially as pre-existing regularization techniques can be easily integrated into current architectures.

The broader implications of this research suggest potential for these methodologies to improve fairness in classification tasks where certain classes are inherently underrepresented. This advancement could significantly affect fields such as ecological studies or autonomous systems, where recognizing rare phenomena or objects is critical yet currently deficient.

Conclusion and Future Work

While the paper provides a promising avenue to enhance long-tailed recognition through regularization, future work could involve studying these balancing techniques in conjunction with sophisticated augmentation methods or within multi-expert models. Expanding the investigation into different regularization norms could unveil further improvements, providing a comprehensive toolkit for addressing long-tailed data challenges.

Overall, this paper contributes significantly to long-tailed recognition research, proposing straightforward yet effective solutions to a persistent problem in AI applications.