Curriculum Learning-Based Approaches for End-to-End Gas Recognition

Abstract

Deep learning has demonstrated great potential in the field of gas recognition using an electronic nose. However, owing to the limited resources of embedded devices, they can only meet the computing and storage requirements of lightweight deep neural network (DNN) models, which reduces the classification accuracy. This study proposes three curriculum learning approaches to improve the performance of lightweight DNN models for end-to-end gas recognition, including a domain knowledge-based approach and two approaches that automatically arrange the curriculum. Among the two automated methods, the first uses the correlation strength between sample responses to measure the difficulty of the training samples, and the second is based on the classification difficulty and disagreements among multiple teacher models. Furthermore, our approaches are combined with a mixup-based data augmentation method to apply curriculum learning to small datasets. The superiority of the proposed approaches was verified using 14 electronic nose datasets. The experimental results demonstrate that these approaches can be used on different types of datasets while boosting the gas recognition accuracy and F1 score of various DNN models by 9.9% and 10.9% on average, respectively, which can guide the future design of high-accuracy gas sensing embedded systems.