SoftTriple Loss: Deep Metric Learning Without Triplet Sampling

• Published in: 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
• Main Authors: Qian, Qi, Shang, Lei, Sun, Baigui, Hu, Juhua, Li, Hao, Jin, Rong
• Format: Text
• Language: unknown
• Published: UW Tacoma Digital Commons 2019
• Subjects: Measurement; task analysis; neural networks; principal component analysis; optimization; benchmark testing; data models; DML
• Online Access: https://digitalcommons.tacoma.uw.edu/tech_pub/366; https://doi.org/10.1109/ICCV.2019.00655

Distance metric learning (DML) is to learn the embeddings where examples from the same class are closer than examples from different classes. It can be cast as an optimization problem with triplet constraints. Due to the vast number of triplet constraints, a sampling strategy is essential for DML. With the tremendous success of deep learning in classifications, it has been applied for DML. When learning embeddings with deep neural networks (DNNs), only a mini-batch of data is available at each iteration. The set of triplet constraints has to be sampled within the mini-batch. Since a mini-batch cannot capture the neighbors in the original set well, it makes the learned embeddings sub-optimal. On the contrary, optimizing SoftMax loss, which is a classification loss, with DNN shows a superior performance in certain DML tasks. It inspires us to investigate the formulation of SoftMax. Our analysis shows that SoftMax loss is equivalent to a smoothed triplet loss where each class has a single center. In real-world data, one class can contain several local clusters rather than a single one, e.g., birds of different poses. Therefore, we propose the SoftTriple loss to extend the SoftMax loss with multiple centers for each class. Compared with conventional deep metric learning algorithms, optimizing SoftTriple loss can learn the embeddings without the sampling phase by mildly increasing the size of the last fully connected layer. Experiments on the benchmark fine-grained data sets demonstrate the effectiveness of the proposed loss function.