用神经架构搜索优化样本重加权网络，提升复杂分布偏移下的模型性能。
Neural Architecture Search of Sample Reweighting Networks for Complex Distribution Shift
- 通过神经架构搜索自动优化重加权网络结构，适配多类型分布偏移。
- 在含标签噪声与类别不平衡的CIFAR-10/100上，准确率显著优于原版MW-Net。
- 适合处理标签噪声和类别不平衡并存场景的鲁棒训练任务。
样本重加权是应对分布偏移（如标签噪声、类别不平衡）的重要方法。Meta-Weight-Net（MW-Net）是一种基于分类损失计算样本权重的有前景网络，虽在单一分布偏移下表现良好，但当同时存在标签噪声与类别不平衡时，仅依赖分类损失难以确定合适权重，且简单网络结构导致性能下降。本研究引入神经架构搜索（NAS），采用树状结构帕尔森估计器（Tree-structured Parzen Estimator），探索最优隐藏层数量与节点数，并选择分类模型中最合适的中间层作为MW-Net输入。在加入标签噪声与类别不平衡的CIFAR-10与CIFAR-100数据集上的实验表明，该方法能有效缓解性能退化，显著提升模型鲁棒性。
原文摘要 · Abstract (English)
Sample reweighting is a major approach to addressing distribution shifts, such as label noise and class imbalance. Meta-Weight-Net (MW-Net) is a promising sample reweighting network that computes weights based on classification loss. Although MW-Net improves prediction performance under a single type of distribution shift using a simple neural network, its performance degrades when facing both label noise and class imbalance, where it is hard to determine appropriate weights solely from classification loss and using a simple network. In this study, we introduce neural architecture search to MW-Net to mitigate such performance degradation. Using the tree-structured Parzen estimator, we explore the optimal number of hidden layers and nodes and select the most suitable intermediate layer in the classification model to serve as the input for MW-Net. Experimental results on the CIFAR-10 and CIFAR-100 datasets that were modified to include both label noise and class imbalance demonstrate the effectiveness of neural architecture search for MW-Net.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。