Deep models, e.g., CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world. However, novel classes emerge from time to time in our ever-changing world, requiring a learning system to acquire new knowledge continually. Class-Incremental Learning (CIL) enables the learner to incorporate the knowledge of new classes incrementally and build a universal classifier among all seen classes. Correspondingly, when directly training the model with new class instances, a fatal problem occurs — the model tends to catastrophically forget the characteristics of former ones, and its performance drastically degrades. There have been numerous efforts to tackle catastrophic forgetting in the machine learning community. In this paper, we survey comprehensively recent advances in class-incremental learning and summarize these methods from several aspects. We also provide a rigorous and unified evaluation of 17 methods in benchmark image classification tasks to find out the characteristics of different algorithms empirically. Furthermore, we notice that the current comparison protocol ignores the influence of memory budget in model storage, which may result in unfair comparison and biased results. Hence, we advocate fair comparison by aligning the memory budget in evaluation, as well as several memory-agnostic performance measures. The source code is available at https://github.com/zhoudw-zdw/CIL_Survey/ .

Class-Incremental Learning (CIL) or continual learning is a desired capability in the real world, which requires a learning system to adapt to new tasks without forgetting former ones. While traditional CIL methods focus on visual information to grasp core features, recent advances in Vision-Language Models (VLM) have shown promising capabilities in learning generalizable representations with the aid of textual information. However, when continually trained with new classes, VLMs often suffer from catastrophic forgetting of former knowledge. Applying VLMs to CIL poses two major challenges: 1) how to adapt the model without forgetting and 2) how to make full use of the multi-modal information. To this end, we propose PROjectiOn Fusion (Proof) that enables VLMs to learn without forgetting. To handle the first challenge, we propose training task-specific projections based on the frozen image/text encoders. When facing new tasks, new projections are expanded, and former projections are fixed, alleviating the forgetting of old concepts. For the second challenge, we propose the fusion module to better utilize the cross-modality information. By jointly adjusting visual and textual features, the model can capture better task-specific semantic information that facilitates recognition. Extensive experiments on nine benchmark datasets with various continual learning scenarios and various VLMs validate that Proof achieves state-of-the-art performance.

Recent years have witnessed growing interests in developing deep models for incremental learning. However, existing approaches often utilize the fixed structure and online backpropagation for deep model optimization, which is difficult to be implemented for incremental data scenarios. Indeed, for streaming data, there are two main challenges for building deep incremental models. First, there is a requirement to develop deep incremental models with Capacity Scalability. In other words, the entire training data are not available before learning the task. It is a challenge to make the deep model structure scaling with streaming data for flexible model evolution and faster convergence. Second, since the stream data distribution usually changes in nature (concept drift), there is a constraint for Capacity Sustainability. That is, how to update the model while preserving previous knowledge for overcoming the catastrophic forgetting. To this end, in this paper, we develop an incremental adaptive deep model (IADM) for dealing with the above two capacity challenges in real-world incremental data scenarios. Specifically, IADM provides an extra attention model for the hidden layers, which aims to learn deep models with adaptive depth from streaming data and enables capacity scalability. Also, we address capacity sustainability by exploiting the attention based fisher information matrix, which can prevent the forgetting in consequence. Finally, we conduct extensive experiments on real-world data and show that IADM outperforms the state-of-the-art methods with a substantial margin. Moreover, we show that IADM has better capacity scalability and sustainability in incremental learning scenarios.