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- W4387560304 abstract "Unlike primates, training artificial neural networks on changing data distributions leads to a rapid decrease in performance on old tasks. This phenomenon is commonly referred to as catastrophic forgetting. In this paper, we investigate the representational changes that underlie this performance decrease and identify three distinct processes that together account for the phenomenon. The largest component is a misalignment between hidden representations and readout layers. Misalignment occurs due to learning on additional tasks and causes internal representations to shift. Representational geometry is partially conserved under this misalignment and only a small part of the information is irrecoverably lost. All types of representational changes scale with the dimensionality of hidden representations. These insights have implications for deep learning applications that need to be continuously updated, but may also aid aligning ANN models to the rather robust biological vision." @default.
- W4387560304 created "2023-10-12" @default.
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- W4387560304 date "2023-10-09" @default.
- W4387560304 modified "2023-10-18" @default.
- W4387560304 title "Diagnosing Catastrophe: Large parts of accuracy loss in continual learning can be accounted for by readout misalignment" @default.
- W4387560304 doi "https://doi.org/10.48550/arxiv.2310.05644" @default.
- W4387560304 hasPublicationYear "2023" @default.
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