Title: Wakening Past Concepts without Past Data: Class-incremental Learning from Placebos. Abstract: Not forgetting knowledge about previous classes is one of the key challenges in class-incremental learning (CIL). A common technique to address this challenge is knowledge distillation (KD) that penalizes inconsistencies across models of subsequent phases. As old-class data is scarce, the KD loss mainly uses new class data. However, we empirically observe that this both harms learning of new classes and also underperforms to distil old class knowledge from the previous phase model. To address this issue, we propose to compute the KD loss using placebo data chosen from a free image stream (e.g., Google Images), which is both simple and surprisingly effective even when there is no class overlap between the placebos and the old data. When the image stream is available, we use an evaluation function to quickly judge the quality of candidate images (good or bad placebos) and collect good ones. For training this function, we sample pseudo CIL tasks from the data in the 0-th phase and design a reinforcement learning algorithm. Our method does not require any additional supervision or memory budget, and can significantly improve a number of top-performing CIL methods, in particular on higher-resolution benchmarks, e.g., ImageNet-1k and ImageNet-Subset, and with a lower memory budget for old class exemplars, e.g., five exemplars per class.