Long-tailed Recognition by Routing Diverse Distribution-Aware Experts (2010.01809v4)

Abstract: Natural data are often long-tail distributed over semantic classes. Existing recognition methods tackle this imbalanced classification by placing more emphasis on the tail data, through class re-balancing/re-weighting or ensembling over different data groups, resulting in increased tail accuracies but reduced head accuracies. We take a dynamic view of the training data and provide a principled model bias and variance analysis as the training data fluctuates: Existing long-tail classifiers invariably increase the model variance and the head-tail model bias gap remains large, due to more and larger confusion with hard negatives for the tail. We propose a new long-tailed classifier called RoutIng Diverse Experts (RIDE). It reduces the model variance with multiple experts, reduces the model bias with a distribution-aware diversity loss, reduces the computational cost with a dynamic expert routing module. RIDE outperforms the state-of-the-art by 5% to 7% on CIFAR100-LT, ImageNet-LT and iNaturalist 2018 benchmarks. It is also a universal framework that is applicable to various backbone networks, long-tailed algorithms, and training mechanisms for consistent performance gains. Our code is available at: https://github.com/frank-xwang/RIDE-LongTailRecognition.

• The paper introduces RIDE, a multi-expert architecture that reduces model variance while closing the head-tail bias gap with a diversity loss.
• It employs dynamic expert routing to selectively allocate resources, achieving a 5-7% performance boost on benchmarks like CIFAR100-LT, ImageNet-LT, and iNaturalist 2018.
• RIDE adapts to various backbone networks, establishing a universal framework that enhances both head and tail class recognition efficiently.

Overview of "Long-tailed Recognition by Routing Diverse Distribution-Aware Experts"

This paper addresses the challenge of long-tailed recognition, where data sets are often imbalanced, with a few classes having a large number of samples (head classes) and many classes with only a few samples (tail classes). Traditional methods typically result in improved accuracy for tail classes at the expense of accuracy in head classes. This new approach, called Routing Diverse Experts (RIDE), aims to balance the trade-off by reducing both model variance and bias.

RIDE introduces a multi-expert architecture where diverse, distribution-aware experts are employed to tackle the imbalance. The key components of this approach are:

• Multiple Experts: RIDE deploys several experts to capture diverse characteristics of data distributions and achieve greater variance reduction.
• Distribution-Aware Diversity Loss: This component minimizes bias by promoting diversity among the experts, thereby reducing potential overfitting to head or tail distributions.
• Dynamic Expert Routing: To minimize computational costs, RIDE dynamically routes each data instance to experts that are most suitable, optimizing resource use without sacrificing accuracy.

The empirical results indicate that RIDE outperforms state-of-the-art methods by 5% to 7% on CIFAR100-LT, ImageNet-LT, and iNaturalist 2018 datasets. It consistently improves classification performance across all classes, showing both strong head and tail accuracy, which previous methods failed to achieve.

Key Contributions and Analysis

1. Bias and Variance Trade-off: The paper conducts a robust analysis of model bias and variance, highlighting that existing methods do not sufficiently close the head-tail bias gap and often increase the variance. RIDE offers a solution by leveraging multiple experts to reduce variance while employing a diversity loss to close the bias gap.
2. Universal Framework: RIDE is generalized to work across various backbone architectures, showcasing its adaptability and consistent performance gains. This universality indicates its practical application potential across different neural network models, such as ResNet, ResNeXt, and Swin Transformers.
3. Efficiency and Scalability: Despite using multiple experts, RIDE manages to maintain, or even reduce, computational costs through its routing strategy, which selectively activates experts based on instance requirements.
4. Theoretical and Applied Implications: The integration of distribution-aware loss and dynamic routing offers a paradigm shift in handling long-tail distributions, challenging the conventional wisdom that improvements in few-shot performance must come at the expense of many-shot performance. RIDE demonstrates that both can be achieved simultaneously.

Future Directions

The success of RIDE in diverse datasets and network architectures paves the way for future explorations into more adaptive and resource-efficient mechanisms for handling long-tail distributions. Future work may investigate adaptive expert selection mechanisms that further minimize computational costs without human intuition or re-tuning.

Additionally, exploring the application of the RIDE framework in other domains beyond image classification, such as NLP or time-series prediction, could reveal new insights and applications. With continual advancements in AI, integrating RIDE's concepts into broader datasets and tasks will likely lead to significant innovations, reinforcing the model's foundational impact on long-tail distribution learning.