Image retrieval methods based on CNN descriptors rely on metric learning from a large number of diverse examples of positive and negative image pairs. Domains, such as night-time images, with limited availability and variability of training data suffer from poor retrieval performance even with methods performing well on standard benchmarks. We propose to train a GAN-based synthetic-image generator, translating available day-time image examples into night images. Such a generator is used in metric learning as a form of augmentation, supplying training data to the scarce domain. Various types of generators are evaluated and analyzed. We contribute with a novel light-weight GAN architecture that enforces the consistency between the original and translated image through edge consistency. The proposed architecture also allows a simultaneous training of an edge detector that operates on both night and day images. To further increase the variability in the training examples and to maximize the generalization of the trained model, we propose a novel method of diverse anchor mining. The proposed method improves over the state-of-the-art results on a standard Tokyo 24/7 day-night retrieval benchmark while preserving the performance on Oxford and Paris datasets. This is achieved without the need of training image pairs of matching day and night images. The source code is available at https://github.com/mohwald/gandtr .

翻译：基于CNN描述符的图像检索方法依赖于从大量多样化的正负图像对样本中进行度量学习。在训练数据可用性和多样性受限的领域（如夜间图像），即使采用在标准基准测试中表现优异的方法，其检索性能仍然较差。我们提出训练一种基于GAN的合成图像生成器，将可用的日间图像样本转换为夜间图像。此类生成器在度量学习中作为一种增强形式，为稀缺领域提供训练数据。我们对多种类型的生成器进行了评估与分析。本文贡献了一种新颖的轻量级GAN架构，通过边缘一致性强制约束原始图像与转换图像之间的对应关系。该架构还支持同步训练适用于昼夜图像的边缘检测器。为进一步增加训练样本的多样性并最大化训练模型的泛化能力，我们提出了一种新颖的多样化锚点挖掘方法。该方法在标准Tokyo 24/7昼夜检索基准上取得了优于现有技术的成果，同时在Oxford和Paris数据集上保持了原有性能。这一成果无需匹配的昼夜图像对训练数据即可实现。源代码发布于https://github.com/mohwald/gandtr。