Semi-supervised few-shot class-incremental learning based on dynamic topology evolution

Incremental learning of new classes is crucial to developing real-world artificial intelligence systems. However, in some cases, we can only use a limited number of images to train the incremental model due to the difficulty of obtaining them. Few-shot class-incremental learning (FSCIL) is a new paradigm of incremental learning, which continually learns new concepts from a few examples. This setup can quickly lead to catastrophic forgetting and overfitting problems, severely affecting model performance. To address these problems, we propose a dynamic topological evolution (DTE) model that unifies knowledge of old and new categories. In this work, we focus on a novel semi-supervised few-shot class-incremental learning (SSFSCIL) to incrementally learn new categories with a limited number of labeled samples and a large number of unlabeled samples. In detail, we use the Voronoi polygon criterion to divide the feature space and construct a Hebbian graph to represent topological relationships between classes. Then, the corresponding k-nearest neighbor subgraph is constructed for the new incremental task, and label propagation of graph nodes is carried out on the unlabeled set to generate the corresponding pseudo labels. Finally, the new category knowledge is evolved into the actual category knowledge by integrating the Hebbian graph and the k-nearest neighbor subgraph obtained in the new incremental task. Extensive experiments on the mini-ImageNet and CIFAR100 datasets show that the accuracy of the DTE outperforms the baseline and improves by 1.41% and 1.13% on the last incremental task. Furthermore, our methodology achieved a 35.6% time improvement compared to full-supervised methods on the CIFAR100 dataset. In addition to the experiments on the two datasets mentioned above, the validation of our proposed DTE model extends to the engineering application domain of the RESICS-45 aircraft image scene classification dataset.