CoReS: Compatible Representations via Stationarity
arXiv:2111.07632 · doi:10.1109/TPAMI.2023.3259542
Abstract
Compatible features enable the direct comparison of old and new learned features allowing to use them interchangeably over time. In visual search systems, this eliminates the need to extract new features from the gallery-set when the representation model is upgraded with novel data. This has a big value in real applications as re-indexing the gallery-set can be computationally expensive when the gallery-set is large, or even infeasible due to privacy or other concerns of the application. In this paper, we propose CoReS, a new training procedure to learn representations that are \textit{compatible} with those previously learned, grounding on the stationarity of the features as provided by fixed classifiers based on polytopes. With this solution, classes are maximally separated in the representation space and maintain their spatial configuration stationary as new classes are added, so that there is no need to learn any mappings between representations nor to impose pairwise training with the previously learned model. We demonstrate that our training procedure largely outperforms the current state of the art and is particularly effective in the case of multiple upgrades of the training-set, which is the typical case in real applications.
in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. Code: https://github.com/NiccoBiondi/cores-compatibility
References in corpus (15)
- How transferable are features in deep neural networks?
- Deep Learning Face Representation by Joint Identification-Verification
- Learning Face Representation from Scratch
- Three scenarios for continual learning
- Prevalence of Neural Collapse during the terminal phase of deep learning training
- A survey of advances in vision-based vehicle re-identification
- Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch
- Understanding Dimensional Collapse in Contrastive Self-supervised Learning
- Regular Polytope Networks
- Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?
- The Met Dataset: Instance-level Recognition for Artworks
- Neural Collapse: A Review on Modelling Principles and Generalization
- Backward-Compatible Prediction Updates: A Probabilistic Approach
- Maximally Compact and Separated Features with Regular Polytope Networks
- Unified Representation Learning for Cross Model Compatibility