Deep long-tailed recognition has been widely studied to address the issue of imbalanced data distributions in real-world scenarios. However, there has been insufficient focus on the design of neural architectures, despite empirical evidence suggesting that architecture can significantly impact performance. In this paper, we attempt to mitigate long-tailed issues through architectural improvements. To simplify the design process, we utilize Differential Architecture Search (DARTS) to achieve this goal. Unfortunately, existing DARTS methods struggle to perform well in long-tailed scenarios. To tackle this challenge, we introduce Long-Tailed Differential Architecture Search (LT-DARTS). Specifically, we conduct extensive experiments to explore architectural components that demonstrate better performance on long-tailed data and propose a new search space based on our observations. This ensures that the architecture obtained through our search process incorporates superior components. Additionally, we propose replacing the learnable linear classifier with an Equiangular Tight Frame (ETF) classifier to further enhance our method. This classifier effectively alleviates the biased search process and prevents performance collapse. Extensive experimental evaluations demonstrate that our approach consistently improves upon existing methods from an orthogonal perspective and achieves state-of-the-art results with simple enhancements.

翻译：深度长尾识别已被广泛研究，以解决现实场景中数据分布不平衡的问题。然而，尽管经验证据表明架构能显著影响性能，但针对神经网络架构设计的关注仍显不足。在本文中，我们尝试通过架构改进来缓解长尾问题。为简化设计过程，我们利用可微分架构搜索（DARTS）来实现这一目标。遗憾的是，现有的DARTS方法在长尾场景中难以表现良好。为应对这一挑战，我们引入了长尾可微分架构搜索（LT-DARTS）。具体而言，我们进行了大量实验，以探索在长尾数据上表现出更好性能的架构组件，并基于我们的观察提出了一种新的搜索空间。这确保了通过我们的搜索过程获得的架构包含了更优的组件。此外，我们提出用等角紧框架（ETF）分类器替代可学习的线性分类器，以进一步增强我们的方法。该分类器有效缓解了搜索过程中的偏差并防止了性能崩溃。大量的实验评估表明，我们的方法从一个正交的视角持续改进了现有方法，并通过简单的增强实现了最先进的结果。