Zhen Ye
ABSTRACT
For remote sensing scene classification (RSSC), exemplar-based class-incremental learning uses all the training data of the new classes and a small number of exemplars of the old classes to train the model in each incremental learning phase, which leads to the failure of fully preserving the historical knowledge as well as the problem of imbalance between the old and new data. In this paper, we compress the exemplars by downsampling the non-discriminative pixels (e.g., the background) to save more compressed exemplars under a fixed memory budget, so that the incremental learning model can alleviate the imbalance problem while better preserving the historical knowledge. Specifically, this compression is achieved by generating 0-1 masks of discriminative pixels from class activation map (CAM). In addition, convolutional block attention module (CBAM) is added to help CAM accurately acquire the locations of discriminative pixels. Furthermore, adaptive mask generation model called class-incremental masking (CIM) adaptively finds optimal thresholds for different classes in class-incremental learning and transforms the heat map of CAM into a 0-1 mask with class-specific thresholds. We conducted experiments on two publicly available remote sensing scene data and the experimental results show that the classification performance of the proposed method outstands that of several state-of-the-art methods.
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Index Terms
- A Class-incremental Learning Method based on Exemplar Compression for Remote Sensing Scene Classification
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