Abstract
Continual learning requires incremental compatibility with a sequence of tasks. However, the design of model architecture remains an open question: In general, learning all tasks with a shared set of parameters suffers from severe interference between tasks; while learning each task with a dedicated parameter subspace is limited by scalability. In this work, we theoretically analyze the generalization errors for learning plasticity and memory stability in continual learning, which can be uniformly upper-bounded by (1) discrepancy between task distributions, (2) flatness of loss landscape and (3) cover of parameter space. Then, inspired by the robust biological learning system that processes sequential experiences with multiple parallel compartments, we propose Cooperation of Small Continual Learners (CoSCL) as a general strategy for continual learning. Specifically, we present an architecture with a fixed number of narrower sub-networks to learn all incremental tasks in parallel, which can naturally reduce the two errors through improving the three components of the upper bound. To strengthen this advantage, we encourage to cooperate these sub-networks by penalizing the difference of predictions made by their feature representations. With a fixed parameter budget, CoSCL can improve a variety of representative continual learning approaches by a large margin (e.g., up to 10.64% on CIFAR-100-SC, 9.33% on CIFAR-100-RS, 11.45% on CUB-200-2011 and 6.72% on Tiny-ImageNet) and achieve the new state-of-the-art performance. Our code is available at https://github.com/lywang3081/CoSCL.
L. Wang and X. Zhang—Contributed equally.
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Notes
- 1.
In contrast to a single continual learning model with a wide network, we refer to such narrower sub-networks as “small” continual learners.
- 2.
A concurrent work observed that the regular CNN architecture indeed achieves better continual learning performance than more advanced architectures such as ResNet and ViT with the same amount of parameters [27].
- 3.
They both are performed against a similar AlexNet-based architecture.
- 4.
Here we only use feature ensemble (FE) with ensemble cooperation loss (EC).
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Acknowledgements
This work was supported by the National Key Research and Development Program of China (2017YFA0700904, 2020AAA0106000, 2020AAA0104304, 2020AAA0106302, 2021YFB2701000), NSFC Projects (Nos. 62061136001, 62106123, 62076147, U19B2034, U1811461, U19A2081, 61972224), Beijing NSF Project (No. JQ19016), BNRist (BNR2022RC01006), Tsinghua-Peking Center for Life Sciences, Tsinghua Institute for Guo Qiang, Beijing Academy of Artificial Intelligence (BAAI), Tsinghua-OPPO Joint Research Center for Future Terminal Technology, the High Performance Computing Center, Tsinghua University, and China Postdoctoral Science Foundation (Nos. 2021T140377, 2021M701892).
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Wang, L., Zhang, X., Li, Q., Zhu, J., Zhong, Y. (2022). CoSCL: Cooperation of Small Continual Learners is Stronger Than a Big One. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13686. Springer, Cham. https://doi.org/10.1007/978-3-031-19809-0_15
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