Incremental acoustic scene classification using rehearsal-based strategy
Ibnu Daqiqil Id, Masanobu Abe, Sunao Hara · 2022 IEEE 11th Global Conference on Consumer Electronics (GCCE) · 2022
Novel class incremental is a special case of concept drift, where a new class exists in future learning. To solve this problem, updating or retraining the model is needed to accommodate new knowledge into the model. However, when the model is retrained using the latest dataset, the performance gradually decreases because the model forgets the previous knowledge. Therefore, a straightforward solution is rehearsal-based learning with all of the past datasets, but some systems have a limitation on the amounts of data saved. This paper proposes a method for selecting the small portion of the past dataset as representative data, and then we use GAN and data augmentation to generate the samples as an extension of the rehearsal data. Experimental results show promising results, prevent catastrophic forgetting, and increase backward transfer. The model performs better when using a low logit sample than a high logit in selecting the representative data and GAN.