通过融合层次结构提升分类模型对细粒度标签的判别能力
Hybrid Losses for Hierarchical Embedding Learning
- 结合树状层级结构设计混合损失函数，区分错误标签远近
- 在近200个细粒度类别上实现更优分类与检索性能
- 适合需要跨类别泛化能力的音频分类任务
传统监督学习中的交叉熵损失对所有错误预测同等对待，忽略错误标签与正确答案之间的相关性或接近程度。本文利用细粒度标签的树状层级结构，研究了广义三元组损失与交叉熵损失等混合损失，在多任务学习框架中强化同一层级标签间的相似性。我们提出评估嵌入空间结构的指标，检验模型对未见类别的泛化能力，即推断未见类别数据的相似类别。在包含近200个细粒度类别的四级层次化乐器声音数据集OrchideaSOL上的实验表明，所提出的混合损失在分类、检索、嵌入空间结构及泛化能力方面均优于现有方法。
原文摘要 · Abstract (English)
In traditional supervised learning, the cross-entropy loss treats all incorrect predictions equally, ignoring the relevance or proximity of wrong labels to the correct answer. By leveraging a tree hierarchy for fine-grained labels, we investigate hybrid losses, such as generalised triplet and cross-entropy losses, to enforce similarity between labels within a multi-task learning framework. We propose metrics to evaluate the embedding space structure and assess the model's ability to generalise to unseen classes, that is, to infer similar classes for data belonging to unseen categories. Our experiments on OrchideaSOL, a four-level hierarchical instrument sound dataset with nearly 200 detailed categories, demonstrate that the proposed hybrid losses outperform previous works in classification, retrieval, embedding space structure, and generalisation.
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