Deep neural networks are powerful tools in learning sophisticated but fixed mapping rules between inputs and outputs, thereby limiting their application in more complex and dynamic situations in which the mapping rules are not kept the same but change according to different contexts. To lift such limits, we developed an approach involving a learning algorithm, called orthogonal weights modification, with the addition of a context-dependent processing module. We demonstrated that with orthogonal weights modification to overcome catastrophic forgetting, and the context-dependent processing module to learn how to reuse a feature representation and a classifier for different contexts, a single network could acquire numerous context-dependent mapping rules in an online and continual manner, with as few as approximately ten samples to learn each. Our approach should enable highly compact systems to gradually learn myriad regularities of the real world and eventually behave appropriately within it.
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All data used in this paper are publicly available and can be accessed at http://yann.lecun.com/exdb/mnist/ for the MNIST dataset, https://www.cs.toronto.edu/~kriz/cifar.html for the CIFAR dataset, http://image-net.org/index for the ILSVR2012 dataset, http://www.nlpr.ia.ac.cn/databases/handwriting/Home.html for the CASIA-HWDB dataset and http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html for the CelebA dataset. For more details of the datasets, please refer to the references cited in the Supplementary Methods.
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The authors thank D. Nikolić for helpful discussions and R. Hadsell for comments on the manuscript. This work was supported by the National Key Research and Development Program of China (2017YFA0105203), the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (XDB32040200), Key Research Program of the National Laboratory of Pattern Recognition (99S9011M2N), and the Hundred-Talent Program of CAS (for S.Y.).
The Institute of Automation, Chinese Academy of Sciences has submitted patent applications on the OWM algorithm (application no. PCT/CN2019/083355; invented by Y.C., G.Z. and S.Y.; pending) and the CDP module (application no. PCT/CN2019/083356; invented by G.Z., Y.C. and S.Y.; pending).
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Zeng, G., Chen, Y., Cui, B. et al. Continual learning of context-dependent processing in neural networks. Nat Mach Intell 1, 364–372 (2019). https://doi.org/10.1038/s42256-019-0080-x
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