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Deep neural networks perform exceptionally well on various learning taskswith state-of-the-art results. While these models are highly expressive andachieve impressively accurate solutions with excellent generalizationabilities, they are susceptible to minor perturbations. Samples that suffersuch perturbations are known as "adversarial examples". Even though deeplearning is an extensively researched field, many questions about the nature ofdeep learning models remain unanswered. In this paper, we introduce a newconceptual framework attached with a formal description that aims to shed lighton the network's behavior and interpret the behind-the-scenes of the learningprocess. Our framework provides an explanation for inherent questionsconcerning deep learning. Particularly, we clarify: (1) Why do neural networksacquire generalization abilities? (2) Why do adversarial examples transferbetween different models?. We provide a comprehensive set of experiments thatsupport this new framework, as well as its underlying theory.

Meet You Halfway: Explaining Deep Learning Mysteries