An Empirical Study of Example Forgetting during Deep Neural Network Learning

Code

• github.com/mtoneva/example_forgetting
  OfficialIn paperpytorch★ 0
• github.com/boone891214/mest
  pytorch★ 17
• github.com/TruongThaoNguyen/kakurenbo
  pytorch★ 7

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.

Tasks

Reproductions