Class-Incremental Learning: Survey and Performance Evaluation on Image Classification

For future learning systems, incremental learning is desirable because it allows for: efficient resource usage by eliminating the need to retrain from scratch at the arrival of new data; reduced memory usage by preventing or limiting the amount of data required to be stored - also important when privacy limitations are imposed; and learning that more closely resembles human learning. In this paper, we provide a complete survey of existing class-incremental learning methods for image classificati...

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• 1 The main challenge for incremental learning is catastrophic forgetting, which refers to the precipitous drop in performance on previously learned tasks after learning a new one.
• 2 Incremental learning of deep neural networks has seen explosive growth in recent years.
• 3 Initial work focused on task-incremental learning, where a task-ID is provided at inference time.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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