  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. For example, a robot needs to
understand new instructions, and an opinion monitoring system should analyze
emerging topics every day. 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 deep class-incremental learning and
summarize these methods from three aspects, i.e., data-centric, model-centric,
and algorithm-centric. We also provide a rigorous and unified evaluation of 16
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 to reproduce these
evaluations is available at https://github.com/zhoudw-zdw/CIL_Survey/

Computer Vision and Pattern Recognition;Machine Learning