Balancing the Causal Effects in Class-Incremental Learning
Junhao Zheng Ruiyan Wang Chongzhi Zhang Huawen Feng Qianli Ma
Class-Incremental Learning (CIL) is a practical and challenging problem for
achieving general artificial intelligence. Recently, Pre-Trained Models (PTMs)
have led to breakthroughs in both visual and natural language processing tasks.
Despite recent studies showing PTMs' potential ability to learn sequentially, a
plethora of work indicates the necessity of alleviating the catastrophic
forgetting of PTMs. Through a pilot study and a causal analysis of CIL, we
reveal that the crux lies in the imbalanced causal effects between new and old
data. Specifically, the new data encourage models to adapt to new classes while
hindering the adaptation of old classes. Similarly, the old data encourages
models to adapt to old classes while hindering the adaptation of new classes.
In other words, the adaptation process between new and old classes conflicts
from the causal perspective. To alleviate this problem, we propose Balancing
the Causal Effects (BaCE) in CIL. Concretely, BaCE proposes two objectives for
building causal paths from both new and old data to the prediction of new and
classes, respectively. In this way, the model is encouraged to adapt to all
classes with causal effects from both new and old data and thus alleviates the
causal imbalance problem. We conduct extensive experiments on continual image
classification, continual text classification, and continual named entity
recognition. Empirical results show that BaCE outperforms a series of CIL
methods on different tasks and settings.