We propose a way to learn visual features that are compatible with previously computed ones even when they have different dimensions and are learned via different neural network architectures and loss functions. Compatible means that, if such features are used to compare images, then "new" features can be compared directly to "old" features, so they can be used interchangeably. This enables visual search systems to bypass computing new features for all previously seen images when updating the embedding models, a process known as backfilling. Backward compatibility is critical to quickly deploy new embedding models that leverage ever-growing large-scale training datasets and improvements in deep learning architectures and training methods. We propose a framework to train embedding models, called backward-compatible training (BCT), as a first step towards backward compatible representation learning. In experiments on learning embeddings for face recognition, models trained with BCT successfully achieve backward compatibility without sacrificing accuracy, thus enabling backfill-free model updates of visual embeddings.
MixBCT: Towards Self-Adapting Backward-Compatible Training
Hunan University（湖南大学）；Intellifusion Inc.（云天励飞公司）；Peking University（北京大学）；Peng Cheng Laboratory（鹏城实验室）；The Hong Kong University of Science and Technology(Guangzhou)（香港科技大学（广州））
$BT^2$: Backward-compatible Training with Basis Transformation
University of California, Berkeley（加州大学伯克利分校）；Cornell University（康奈尔大学）；University of Maryland, College Park（马里兰大学帕克分校）；University of Hong Kong（香港大学）；MIT（麻省理工学院）；Meta AI
A Forward and Backward Compatible Framework for Few-shot Class-incremental Pill Recognition
National University of Defense Technology (NUDT)（国防科技大学）；College of Intelligence Science and Technology（智能科学与技术学院）；College of Electronic Science and Technology（电子科学与技术学院）
Improving Forward Compatibility in Class Incremental Learning by Increasing Representation Rank and Feature Richness
LINE Investment Technologies（LINE投资技术公司）；Seoul National University（首尔大学）；LG AI Research（LG人工智能研究院）；Research Institute for Convergence Science, Seoul National University（首尔大学融合科学研究院）
