Hyperspherical Learning in Multi-Label Classification
"Learning from online data with noisy web labels is gaining more attention due to the increasing cost of fully annotated datasets in large-scale multi-label classification tasks. Partial (positive) annotated data, as a particular case of data with noisy labels, are economically accessible. And they serve as benchmarks to evaluate the learning capacity of state-of-the-art methods in real scenarios, though they contain a large number of samples with false negative labels. Existing (partial) multi-label methods are usually studied in the Euclidean space, where the relationship between the label embeddings and image features is not symmetrical and thus can be challenging to learn. To alleviate this problem, we propose reformulating the task into a hyperspherical space, where an angular margin can be incorporated into a hyperspherical multi-label loss function. This margin allows us to effectively balance the impact of false negative and true positive labels. We further design a mechanism to tune the angular margin and scale adaptively. We investigate the effectiveness of our method under three multi-label scenarios (single positive labels, partial positive labels and full labels) on four datasets (VOC12, COCO, CUB-200 and NUS-WIDE). In the single and partial positive labels scenarios, our method achieves state-of-the-art performance. The robustness of our method is verified by comparing the performances at different proportions of partial positive labels in the datasets. Our method also obtains more than 1\% improvement over the BCE loss even on the fully annotated scenario. Analysis shows that the learned label embeddings potentially correspond to actual label correlation, since in hyperspherical space label embeddings and image features are symmetrical and interchangeable. This further indicates the geometric interpretability of our method. Code is available at https://github.com/TencentYoutuResearch/MultiLabel-HML."