In image retrieval, standard evaluation metrics rely on score ranking, e.g. average precision (AP). In this paper, we introduce a method for robust and decomposable average precision (ROADMAP) addressing two major challenges for end-to-end training of deep neural networks with AP: non-differentiability and non-decomposability. Firstly, we propose a new differentiable approximation of the rank function, which provides an upper bound of the AP loss and ensures robust training. Secondly, we design a simple yet effective loss function to reduce the decomposability gap between the AP in the whole training set and its averaged batch approximation, for which we provide theoretical guarantees. Extensive experiments conducted on three image retrieval datasets show that ROADMAP outperforms several recent AP approximation methods and highlight the importance of our two contributions. Finally, using ROADMAP for training deep models yields very good performances, outperforming state-of-the-art results on the three datasets.
翻译:在图像检索方面,标准评价指标依赖于得分等级,例如平均精确度。在本文件中,我们采用了一种稳健和分解平均精确度的方法(ROADMAP),用以应对与AP公司一道对深神经网络进行端到端培训的两大挑战:无差异性和不兼容性。首先,我们提议对排名函数采用新的可区别近似值,为AP公司损失提供上层约束并确保强有力的培训。第二,我们设计了一个简单而有效的损失功能,以缩小AP公司在整个培训中与平均批次近似之间的不兼容性差距,为此我们提供了理论保证。对三套图像检索数据集进行的广泛实验表明,ROADMAP方案超越了最近几个AP近似方法,并突出了我们两项贡献的重要性。最后,我们利用ROADMAPA方案培训深层模型取得非常出色的业绩,在三个数据集上表现优异。