Class Incremental Learning for Image Classification With Out-of-Distribution Task Identification

Class Incremental Learning (CIL) for image classification aims to address real-world scenarios by allowing a model to learn new categories while retaining the knowledge of old categories. It is more challenging than Task Incremental Learning (TIL) as task ID is not provided during testing. Therefore, transitioning from CIL to TIL is an intuitive approach to handling CIL problems for image classification. Currently, the main challenge of this approach lies in improving the accuracy of task identification. To address this issue, we propose to use a large-scale image-text pre-training model (i.e. CLIP) as the backbone, training and saving different classifiers for different tasks. Each classifier not only includes the classes of the current task, but also an Out-of-distribution (OOD) class corresponding to the classes encountered in all previous tasks. At test time, we iterate through classifiers from the last task to find the correct task ID of the test image, and perform classification in a TIL way. In addition, to tackle the issue of early-stop termination in iterative prediction due to model bias toward later tasks, we propose using CLIP zero-shot ability to assist learned OOD detection. Experiments show that our method achieves state-of-the-art performance on the traditional many-shot and the more challenging few-shot settings of CIFAR-100 and ImageNet-Subset datasets.