FastFill：高效的兼容模型更新
FastFill: Efficient Compatible Model Update
摘要：
在许多检索系统中，原始高维数据（例如，图像）通过学习的嵌入模型被映射到较低维度的特征。从图库集合中检索与给定查询数据最相似的数据的任务是通过对特征的相似性比较来执行的。当嵌入模型被更新时，它可能会产生与已经在用旧模型计算的图库中的特征不可比/不兼容的特征。随后，图库中的所有特征都需要使用新的嵌入模型重新计算--这是一个计算成本很高的过程，称为嵌入。最近，兼容的表示学习方法已经被提出来避免混淆。尽管它们相对成功，但在新模型的性能及其与旧模型的兼容性之间存在固有的权衡。在这项工作中，我们介绍FastFill：一个兼容的模型更新过程中使用的功能对齐和基于策略的部分重复，以及时提高检索性能。我们发现，以前的训练策略遭受性能下降，并证明了训练目标和在线部分训练中的排序的重要性。我们提出了一种新的训练方法，使用不确定性估计的新旧嵌入模型之间的特征对齐。与以前的工作相比，我们在各种数据集上获得了显着改进的搜索结果：ImageNet上的mAP（+4.4\%），Places-365（+2.7\%）和VGG-Face 2（+1.3\%）。此外，我们证明了当使用FastFill更新有偏模型时，少数子组准确性差距会随着一小部分的部分重复而迅速消失。
英文摘要：
In many retrieval systems the original high dimensional data (e.g., images) is mapped to a lower dimensional feature through a learned embedding model. The task of retrieving the most similar data from a gallery set to a given query data is performed through a similarity comparison on features. When the embedding model is updated, it might produce features that are not comparable/compatible with features already in the gallery computed with the old model. Subsequently, all features in the gallery need to be re-computed using the new embedding model -- a computationally expensive process called backfilling. Recently, compatible representation learning methods have been proposed to avoid backfilling. Despite their relative success, there is an inherent trade-off between the new model performance and its compatibility with the old model. In this work, we introduce FastFill: a compatible model update process using feature alignment and policy based partial backfilling to promptly elevate retrieval performance. We show that previous backfilling strategies suffer from decreased performance and demonstrate the importance of both the training objective and the ordering in online partial backfilling. We propose a new training method for feature alignment between old and new embedding models using uncertainty estimation. Compared to previous works, we obtain significantly improved backfilling results on a variety of datasets: mAP on ImageNet (+4.4\%), Places-365 (+2.7\%), and VGG-Face2 (+1.3\%). Further, we demonstrate that when updating a biased model with FastFill, the minority subgroup accuracy gap promptly vanishes with a small fraction of partial backfilling.
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