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Independently trained machine learning models tend to learn similar features.Given an ensemble of independently trained models, this results in correlatedpredictions and common failure modes. Previous attempts focusing ondecorrelation of output predictions or logits yielded mixed results,particularly due to their reduction in model accuracy caused by conflictingoptimization objectives. In this paper, we propose the novel idea of utilizingmethods of the representational similarity field to promote dissimilarityduring training instead of measuring similarity of trained models. To this end,we promote intermediate representations to be dissimilar at different depthsbetween architectures, with the goal of learning robust ensembles with disjointfailure modes. We show that highly dissimilar intermediate representationsresult in less correlated output predictions and slightly lower errorconsistency, resulting in higher ensemble accuracy. With this, we shine firstlight on the connection between intermediate representations and their impacton the output predictions.

Exploring new ways: Enforcing representational dissimilarity to learn new features and reduce error consistency