Resolving Task Confusion in Dynamic Expansion Architectures for Class
Incremental Learning
- URL: http://arxiv.org/abs/2212.14284v1
- Date: Thu, 29 Dec 2022 12:26:44 GMT
- Title: Resolving Task Confusion in Dynamic Expansion Architectures for Class
Incremental Learning
- Authors: Bingchen Huang, Zhineng Chen, Peng Zhou, Jiayin Chen, Zuxuan Wu
- Abstract summary: Task Correlated Incremental Learning (TCIL) is proposed to encourage discriminative and fair feature utilization across tasks.
TCIL performs a multi-level knowledge distillation to propagate knowledge learned from old tasks to the new one.
The results demonstrate that TCIL consistently achieves state-of-the-art accuracy.
- Score: 27.872317837451977
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The dynamic expansion architecture is becoming popular in class incremental
learning, mainly due to its advantages in alleviating catastrophic forgetting.
However, task confusion is not well assessed within this framework, e.g., the
discrepancy between classes of different tasks is not well learned (i.e.,
inter-task confusion, ITC), and certain priority is still given to the latest
class batch (i.e., old-new confusion, ONC). We empirically validate the side
effects of the two types of confusion. Meanwhile, a novel solution called Task
Correlated Incremental Learning (TCIL) is proposed to encourage discriminative
and fair feature utilization across tasks. TCIL performs a multi-level
knowledge distillation to propagate knowledge learned from old tasks to the new
one. It establishes information flow paths at both feature and logit levels,
enabling the learning to be aware of old classes. Besides, attention mechanism
and classifier re-scoring are applied to generate more fair classification
scores. We conduct extensive experiments on CIFAR100 and ImageNet100 datasets.
The results demonstrate that TCIL consistently achieves state-of-the-art
accuracy. It mitigates both ITC and ONC, while showing advantages in battle
with catastrophic forgetting even no rehearsal memory is reserved.
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