Incremental Scene Classification Using Dual Knowledge Distillation and Classifier Discrepancy on Natural and Remote Sensing Images

Conventional deep neural networks face challenges in handling the increasing amount of information in real-world scenarios where it is impractical to gather all the training data at once. Incremental learning, also known as continual learning, provides a solution for lightweight and sustainable learning with neural networks. However, incremental learning encounters issues such as “catastrophic forgetting” and the “stability–plasticity dilemma”. To address these challenges, this study proposes a two-stage training method. In the first stage, dual knowledge distillation is introduced, including feature map-based and response-based knowledge distillation. This approach prevents the model from excessively favoring new tasks during training, thus addressing catastrophic forgetting. In the second stage, an out-of-distribution dataset is incorporated to calculate the discrepancy loss between multiple classifiers. By maximizing the discrepancy loss and minimizing the cross-entropy loss, the model improves the classification accuracy of new tasks. The proposed method is evaluated using the CIFAR100 and RESISC45 benchmark datasets, comparing it to existing approaches. Experimental results demonstrate an overall accuracy improvement of 6.9% and a reduction of 5.1% in the forgetting rate after adding nine consecutive tasks. These findings indicate that the proposed method effectively mitigates catastrophic forgetting and provides a viable solution for image classification in natural and remote sensing images.