An Empirical Study of Example Forgetting during Deep Neural Network Learning

  • Mariya Toneva ,
  • ,
  • Remi Tachet des Combes ,
  • Adam Trischler ,
  • Yoshua Bengio ,
  • Geoff Gordon

ICLR 2019 |

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.