Angular LDA for Learning After Deployment

Traditional machine learning models are static and once deployed, they no longer can acquire new knowledge. However, real-world environments are constantly evolving, and deployed AI systems will inevitably encounter classes and situations that were not present during training. To remain effective, these systems must be capable of post-deployment learning: identifying novel classes, acquiring ground-truth supervision, and learning them incrementally on the fly from the new data without relying on retraining with both past and newly collected data. To address this challenge, we propose LAFA, a novel post-deployment learning method that leverages pre-trained models and angular linear discriminant analysis with feature and test-time enhancements. Extensive experiments on eight datasets using three backbones demonstrate that LAFA consistently outperforms strong baselines without saving any past training data. Its performance can match that of offline retraining (or joint training) that has access to all past and present data. We believe the proposed approach is sufficiently mature for practical deployment.