In this paper, we propose a novel method to learn internal feature representation models that are \textit{compatible} with previously learned ones. Compatible features enable for direct comparison of old and new learned features, allowing them to be used interchangeably over time. This eliminates the need for visual search systems to extract new features for all previously seen images in the gallery-set when sequentially upgrading the representation model. Extracting new features is typically quite expensive or infeasible in the case of very large gallery-sets and/or real time systems (i.e., face-recognition systems, social networks, life-long learning systems, robotics and surveillance systems). Our approach, called Compatible Representations via Stationarity (CoReS), achieves compatibility by encouraging stationarity to the learned representation model without relying on previously learned models. Stationarity allows features' statistical properties not to change under time shift so that the current learned features are inter-operable with the old ones. We evaluate single and sequential multi-model upgrading in growing large-scale training datasets and we show that our method improves the state-of-the-art in achieving compatible features by a large margin. In particular, upgrading ten times with training data taken from CASIA-WebFace and evaluating in Labeled Face in the Wild (LFW), we obtain a 49\% increase in measuring the average number of times compatibility is achieved, which is a 544\% relative improvement over previous state-of-the-art.

翻译：摘要：本文提出了一种新颖方法，用于学习与先前已学习特征表示模型相兼容的内部特征表示模型。兼容性特征可直接比较新旧学习特征，使其能够随时间推移互换使用。这消除了视觉搜索系统在顺序升级表示模型时，需为图库集中所有先前图像提取新特征的需求。对于超大规模图库集和/或实时系统（如人脸识别系统、社交网络、终身学习系统、机器人和监控系统），提取新特征通常成本高昂或不可行。我们提出的方法称为"基于平稳性的兼容表示"（CoReS），通过促使学习到的表示模型具有平稳性来实现兼容，且不依赖于先前学习的模型。平稳性使特征的统计特性不会随时间偏移而变化，从而使当前学习特征与旧特征可互操作。我们评估了在增长的大规模训练数据集上进行单次和顺序多模型升级的效果，结果表明我们的方法在实现兼容特征方面大幅提升了现有技术水平。具体而言，使用CASIA-WebFace训练数据进行十次升级，并在Labelled Face in the Wild（LFW）数据集上评估，我们实现兼容性的平均次数提升了49%，相对于先前最优方法达到了544%的相对改进。