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Designing neural network architectures is a challenging task and knowingwhich specific layers of a model must be adapted to improve the performance isalmost a mystery. In this paper, we introduce a novel theory and metric toidentify layers that decrease the test accuracy of the trained models, thisidentification is done as early as at the beginning of training. In theworst-case, such a layer could lead to a network that can not be trained atall. More precisely, we identified those layers that worsen the performancebecause they produce conflicting training bundles as we show in our noveltheoretical analysis, complemented by our extensive empirical studies. Based onthese findings, a novel algorithm is introduced to remove performancedecreasing layers automatically. Architectures found by this algorithm achievea competitive accuracy when compared against the state-of-the-artarchitectures. While keeping such high accuracy, our approach drasticallyreduces memory consumption and inference time for different computer visiontasks.

Conflicting Bundles: Adapting Architectures Towards the Improved Training of Deep Neural Networks