Unshuffling Data for Improved Generalization

27 Feb 2020Damien TeneyEhsan AbbasnejadAnton van den Hengel

The inability to generalize beyond the distribution of a training set is at the core of practical limits of machine learning. We show that the common practice of mixing and shuffling training examples when training deep neural networks is not optimal... (read more)

PDF Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet