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Overfit is a fundamental problem in machine learning in general, and in deeplearning in particular. In order to reduce overfit and improve generalizationin the classification of images, some employ invariance to a group oftransformations, such as rotations and reflections. However, since not allobjects exhibit necessarily the same invariance, it seems desirable to allowthe network to learn the useful level of invariance from the data. To this end,motivated by self-supervision, we introduce an architecture enhancement forexisting neural network models based on input transformations, termed'TransNet', together with a training algorithm suitable for it. Our model canbe employed during training time only and then pruned for prediction, resultingin an equivalent architecture to the base model. Thus pruned, we show that ourmodel improves performance on various data-sets while exhibiting improvedgeneralization, which is achieved in turn by enforcing soft invariance on theconvolutional kernels of the last layer in the base model. Theoretical analysisis provided to support the proposed method.

More Is More -- Narrowing the Generalization Gap by Adding Classification Heads