Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition (2107.09249v4)

Abstract: Existing long-tailed recognition methods, aiming to train class-balanced models from long-tailed data, generally assume the models would be evaluated on the uniform test class distribution. However, practical test class distributions often violate this assumption (e.g., being either long-tailed or even inversely long-tailed), which may lead existing methods to fail in real applications. In this paper, we study a more practical yet challenging task, called test-agnostic long-tailed recognition, where the training class distribution is long-tailed while the test class distribution is agnostic and not necessarily uniform. In addition to the issue of class imbalance, this task poses another challenge: the class distribution shift between the training and test data is unknown. To tackle this task, we propose a novel approach, called Self-supervised Aggregation of Diverse Experts, which consists of two strategies: (i) a new skill-diverse expert learning strategy that trains multiple experts from a single and stationary long-tailed dataset to separately handle different class distributions; (ii) a novel test-time expert aggregation strategy that leverages self-supervision to aggregate the learned multiple experts for handling unknown test class distributions. We theoretically show that our self-supervised strategy has a provable ability to simulate test-agnostic class distributions. Promising empirical results demonstrate the effectiveness of our method on both vanilla and test-agnostic long-tailed recognition. Code is available at \url{https://github.com/Vanint/SADE-AgnosticLT}.

• The paper introduces SADE, a novel method that trains three diverse experts using distinct loss functions to address forward, uniform, and inverse long-tailed distributions.
• It employs a test-time self-supervised aggregation strategy that stabilizes predictions across augmented views to adapt to unknown test class distributions.
• Empirical results show robust improvements, with SADE outperforming state-of-the-art methods by over 2% on ImageNet-LT and demonstrating strong performance on CIFAR100-LT and iNaturalist 2018.

Essay on "Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition"

This paper presents a novel method for tackling the challenge of test-agnostic long-tailed recognition, where the test class distribution is unknown and potentially different from the training distribution. Traditional long-tailed recognition assumes a uniform test class distribution, which often fails in practice where actual test distributions can be long-tailed or inversely long-tailed. This paper overcomes these limitations by proposing a model termed Self-supervised Aggregation of Diverse Experts (SADE).

Key Contributions and Methodology

The methodology hinges on two main strategies: skill-diverse expert learning and test-time self-supervised aggregation. The novelty of SADE's approach to expert learning lies in the training of multiple experts with distinct expertise. Specifically, three experts are trained: a forward expert using a cross-entropy loss for long-tailed class distribution, a uniform expert with a balanced softmax loss to simulate uniform distribution, and a backward expert guided by an inverse softmax loss to address inversely long-tailed distributions. This design exploits the diversity of learning objectives to capture varied class distribution scenarios.

The second major innovation is the test-time self-supervised aggregation strategy, which adapts to unknown test distributions by optimizing the stability of predictions across differently augmented views of test samples. This method adjusts the aggregation weights of multiple experts at test time to align better with the actual test class distribution, thereby enhancing model robustness without prior knowledge of the test environment.

Numerical Results and Insights

The empirical results reported demonstrate that SADE outperforms current long-tailed recognition methods across several benchmark datasets including ImageNet-LT, CIFAR100-LT, and iNaturalist 2018. Notably, SADE exhibits superior performance on both vanilla and test-agnostic scenarios. For instance, it achieves a significant improvement over state-of-the-art methods like RIDE and ACE by more than 2% on ImageNet-LT, reaching an accuracy of 58.8%. This performance indicates the effectiveness of the diverse expert aggregation and improved adaptability to test distribution shifts.

Theoretical and Practical Implications

The theoretical analysis of SADE shows that maximizing prediction stability, as employed in the self-supervised strategy, correlates with maximizing mutual information between predicted and true class distributions while minimizing entropy. This theoretical underpinning explains the method's ability to generalize well across different test scenarios without knowing the class distribution in advance.

Practically, the implication of this work is profound, providing a robust solution that could be deployed in real-world applications like autonomous driving, where environmental conditions and object class frequencies can drastically differ from training datasets.

Future Developments

Future research could explore extending the SADE framework beyond classification tasks to object detection and segmentation. Moreover, while the model complexity and expert training are well justified for the tested scenarios, there is potential to explore more efficient architectures or training strategies to further enhance scalability and deployment efficiency in resource-constrained environments.

In conclusion, the paper presents a well-rounded approach to long-tailed recognition under uncertainty, achieving improved performance by innovatively leveraging the diversity of expert models and employing a sophisticated self-supervised aggregation strategy. The implications are significant for both theoretical advancements and practical applications in AI.