Deep Nearest Class Mean Model for Incremental Odor Classification
This addresses the problem of dynamic odor recognition for applications where datasets grow incrementally, representing an incremental improvement over static methods.
The paper tackles incremental odor classification where new classes emerge over time, proposing a Deep Nearest Class Mean model that dynamically integrates new classes and shows efficiency, especially with few training examples for new classes.
In recent years, more machine learning algorithms have been applied to odor classification. These odor classification algorithms usually assume that the training datasets are static. However, for some odor recognition tasks, new odor classes continually emerge. That is, the odor datasets are dynamically growing while both training samples and number of classes are increasing over time. Motivated by this concern, this paper proposes a Deep Nearest Class Mean (DNCM) model based on the deep learning framework and nearest class mean method. The proposed model not only leverages deep neural network to extract deep features, but is also able to dynamically integrate new classes over time. In our experiments, the DNCM model was initially trained with 10 classes, then 25 new classes are integrated. Experiment results demonstrate that the proposed model is very efficient for incremental odor classification, especially for new classes with only a small number of training examples.