Post Hoc Error Correction for Missing Classes in Deep Neural Networks

This paper presents a novel post hoc error correction method that enables deep neural networks to recognize classes that were completely excluded during training. Unlike traditional approaches requiring full model retraining, our method uses hidden layer representations from any pre-trained classifier to detect and correct errors on missing categories. We demonstrate the approach on facial emotion recognition using the RAF-DB dataset, systematically excluding each of the seven emotion classes from training. The results show correction gains of up to 0.811 for excluded classes while maintaining 99% retention on known classes in the best setup. The method provides a computationally efficient alternative to retraining when new categories emerge after deployment.