多氧代用助力器的深米学习性能表现丧失和有效性 (Multi Proxy Anchor Loss and Effectiveness of Deep Metric Learning Performance Metrics)

Deep metric learning (DML) learns the mapping, which maps into embedding space in which similar data is near and dissimilar data is far. In this paper, we propose the new proxy-based loss and the new DML performance metric. This study contributes two following: (1) we propose multi-proxies anchor (MPA) loss, and we show the effectiveness of the multi-proxies approach on proxy-based loss. (2) we establish the good stability and flexible normalized discounted cumulative gain (nDCG@k) metric as the effective DML performance metric. Finally, we demonstrate MPA loss's effectiveness, and MPA loss achieves new state-of-the-art performance on two datasets for fine-grained images.

翻译：深度计量学习(DML)学会了映射图,地图映射到类似数据接近和不同数据非常接近的嵌入空间,在本文中,我们提出新的代用损失和新的DML性能衡量标准,这一研究提供了以下两个方面:(1) 我们提出多代用锚(MPA)损失,我们显示了对代用损失的多代用锚(MPA)方法的有效性。(2) 我们确立良好的稳定性和灵活的标准化折价累积收益(nDCG@k)衡量标准,作为DML的有效性能衡量标准。最后,我们展示了MPA损失的有效性,MPA损失在两个数据集上取得了新的最新效果,用于微粒图像。