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Some like it tough: Improving model generalization via progressively increasing the training difficulty

Fassold, Hannes;
Open Access
  • Publisher: Zenodo
Abstract
In this work, we propose to progressively increase the training difficulty during learning a neural network model via a novel strategy which we call mini-batch trimming. This strategy makes sure that the optimizer puts its focus in the later training stages on the more difficult samples, which we identify as the ones with the highest loss in the current mini-batch. The strategy is very easy to integrate into an existing training pipeline and does not necessitate a change of the network model. Experiments on several image classification problems show that mini-batch trimming is able to increase the generalization ability (measured via final test error) of the tra...
Persistent Identifiers
Funded by
EC| AI4Media
Project
AI4Media
A European Excellence Centre for Media, Society and Democracy
  • Funder: European Commission (EC)
  • Project Code: 951911
  • Funding stream: H2020
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ZENODO
Conference object
Provider: ZENODO
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