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- W4225830830 abstract "We propose an effective regularization strategy (CW-TaLaR) for solving continual learning problems. It uses a penalizing term expressed by the Cramer-Wold distance between two probability distributions defined on a target layer of an underlying neural network that is shared by all tasks, and the simple architecture of the Cramer-Wold generator for modeling output data representation. Our strategy preserves target layer distribution while learning a new task but does not require remembering previous tasks' datasets. We perform experiments involving several common supervised frameworks, which prove the competitiveness of the CW-TaLaR method in comparison to a few existing state-of-the-art continual learning models." @default.
- W4225830830 created "2022-05-05" @default.
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- W4225830830 date "2021-11-15" @default.
- W4225830830 modified "2023-09-29" @default.
- W4225830830 title "Target Layer Regularization for Continual Learning Using Cramer-Wold Generator" @default.
- W4225830830 hasPublicationYear "2021" @default.
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