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- W2903996579 abstract "Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks. Our goal is to understand whether a related phenomenon occurs when data does not undergo a clear distributional shift. We define a “forgetting event” to have occurred when an individual training example transitions from being classified correctly to incorrectly over the course of learning. Across several benchmark data sets, we find that: (i) certain examples are forgotten with high frequency, and some not at all; (ii) a data set’s (un)forgettable examples generalize across neural architectures; and (iii) based on forgetting dynamics, a significant fraction of examples can be omitted from the training data set while still maintaining state-of-the-art generalization performance." @default.
- W2903996579 created "2018-12-22" @default.
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- W2903996579 date "2018-09-27" @default.
- W2903996579 modified "2023-09-29" @default.
- W2903996579 title "An Empirical Study of Example Forgetting during Deep Neural Network Learning" @default.
- W2903996579 hasPublicationYear "2018" @default.
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