Attention-based Ensemble for Deep Metric Learning
This work addresses the need for more effective ensemble methods in deep metric learning, particularly for image retrieval tasks, though it appears incremental as it builds on existing ensemble approaches.
The paper tackles the problem of improving diversity in ensemble deep metric learning by proposing an attention-based ensemble with multiple attention masks and a divergence loss, resulting in state-of-the-art performance on image retrieval benchmarks with a significant margin.
Deep metric learning aims to learn an embedding function, modeled as deep neural network. This embedding function usually puts semantically similar images close while dissimilar images far from each other in the learned embedding space. Recently, ensemble has been applied to deep metric learning to yield state-of-the-art results. As one important aspect of ensemble, the learners should be diverse in their feature embeddings. To this end, we propose an attention-based ensemble, which uses multiple attention masks, so that each learner can attend to different parts of the object. We also propose a divergence loss, which encourages diversity among the learners. The proposed method is applied to the standard benchmarks of deep metric learning and experimental results show that it outperforms the state-of-the-art methods by a significant margin on image retrieval tasks.