从架构视角重探长尾分布问题，提出高效优化方法。
Revisiting Long-Tailed Learning: Insights from an Architectural Perspective
- 分析网络结构组件对长尾识别的影响，发现关键设计因素。
- 提出两种专为长尾数据优化的卷积操作，提升模型性能。
- 设计专用NAS方法LT-DARTS，适配长尾场景，效果领先。
长尾识别广泛研究以应对真实场景中数据分布不均的问题。然而，针对长尾设置的神经网络架构设计却关注不足，尽管已有证据表明架构选择会显著影响性能。本文旨在弥合长尾挑战与网络设计之间的差距，深入分析不同网络组件（如拓扑结构、卷积方式、激活函数）对长尾识别的影响。基于观察结果，我们提出了两种专为长尾数据优化的卷积操作。考虑到操作间交互对网络有效性的重要性，我们采用神经架构搜索（NAS）进行高效探索，提出适用于长尾数据的新型搜索空间与策略——LT-DARTS。实验表明，该方法在多个长尾数据集上持续优于现有架构，与当前主流长尾方法结合时，实现参数高效、领先的性能表现。
原文摘要 · Abstract (English)
Long-Tailed (LT) recognition has been widely studied to tackle the challenge of imbalanced data distributions in real-world applications. However, the design of neural architectures for LT settings has received limited attention, despite evidence showing that architecture choices can substantially affect performance. This paper aims to bridge the gap between LT challenges and neural network design by providing an in-depth analysis of how various architectures influence LT performance. Specifically, we systematically examine the effects of key network components on LT handling, such as topology, convolutions, and activation functions. Based on these observations, we propose two convolutional operations optimized for improved performance. Recognizing that operation interactions are also crucial to network effectiveness, we apply Neural Architecture Search (NAS) to facilitate efficient exploration. We propose LT-DARTS, a NAS method with a novel search space and search strategy specifically designed for LT data. Experimental results demonstrate that our approach consistently outperforms existing architectures across multiple LT datasets, achieving parameter-efficient, state-of-the-art results when integrated with current LT methods.
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