Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer

By Sen Lin, Li Yang, Deliang Fan, Junshan Zhang. 2022

81 citations recorded by the source corpus.

By learning a sequence of tasks continually, an agent in continual learning (CL) can improve the learning performance of both a new task and `old' tasks by leveraging the forward knowledge transfer and the backward knowledge transfer, respectively. However, most existing CL methods focus on addressing catastrophic forgetting in neural networks by minimizing the modification of the learnt model for old tasks. This inevitably limits the backward knowledge transfer from the new task to the old tasks, because judicious model updates could possibly improve the learning performance of the old tasks as well. To tackle this problem, we first theoretically analyze the conditions under which updating the learnt model of old tasks could be beneficial for CL and also lead to backward knowledge transfer, based on the gradient projection onto the input subspaces of old tasks. Building on the theoretical analysis, we next develop a ContinUal learning method with Backward knowlEdge tRansfer (CUBER), for a fixed capacity neural network without data replay. In particular, CUBER first characterizes the task correlation to identify the positively correlated old tasks in a layer-wise manner, and then selectively modifies the learnt model of the old tasks when learning the new task. Experimental studies show that CUBER can even achieve positive backward knowledge transfer on several existing CL benchmarks for the first time without data replay, where the related baselines still suffer from catastrophic forgetting (negative backward knowledge transfer). The superior performance of CUBER on the backward knowledge transfer also leads to higher accuracy accordingly.

Subfields: Meta & Continual Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Meta & Continual Learning

1. Xingxing Zhang
2. Liyuan Wang
3. Hang Su
4. Jun Zhu
5. René Vidal
6. Tatsuya Konishi
7. Gyuhak Kim
8. Changnan Xiao

Citations in the corpus

• A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “CUBER [258] selectively projects gradients to update the knowledge of old tasks that are positively related to the new task.”
• Learnability and Algorithm for Continual Learning (2023, methodology): “Orthogonal projection methods learn each task in an orthogonal space to other tasks to reduce task interference or CF (Zeng et al., 2019; Kim & Liu, 2020; Chaudhry et al., 2020; Lin et al., 2022).”
• The Ideal Continual Learner: An Agent That Never Forgets (2023, background): “Put differently, the ability of a learner to improve the performance on previous tasks with knowledge from new tasks is desired (only) when previous tasks are solved sub-optimally; such ability is typically called positive backward transfer (Lin et al., 2022b).”