Recently, ensemble has been applied to deep metric learning to yield state-of-the-art results. Deep metric learning aims to learn deep neural networks for feature embeddings, distances of which satisfy given constraint. In deep metric learning, ensemble takes average of distances learned by multiple learners. 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.
翻译:最近,在深入的计量学习中应用了共性,以产生最新的结果。深度的计量学习旨在学习深神经网络,以进行特征嵌入,其距离满足一定的制约。在深度的计量学习中,共性需要多个学习者平均学习的距离。作为共同性的一个重要方面,学生的特征嵌入应多种多样。为此,我们建议采用基于关注的共性,使用多重关注面罩,使每个学习者能够参与不同部分的物体。我们还提议了差异损失,鼓励学习者的多样性。拟议的方法适用于深度衡量学习的标准基准和实验结果,表明在图像检索任务上,它比最新的方法要好得多。