Meet You Halfway: Explaining Deep Learning Mysteries
This addresses fundamental mysteries in deep learning for researchers and practitioners, though it appears incremental as it builds on existing knowledge without claiming broad SOTA breakthroughs.
The paper tackles the problem of explaining why deep neural networks generalize and why adversarial examples transfer between models, by introducing a new conceptual framework with formal descriptions and experimental support.
Deep neural networks perform exceptionally well on various learning tasks with state-of-the-art results. While these models are highly expressive and achieve impressively accurate solutions with excellent generalization abilities, they are susceptible to minor perturbations. Samples that suffer such perturbations are known as "adversarial examples". Even though deep learning is an extensively researched field, many questions about the nature of deep learning models remain unanswered. In this paper, we introduce a new conceptual framework attached with a formal description that aims to shed light on the network's behavior and interpret the behind-the-scenes of the learning process. Our framework provides an explanation for inherent questions concerning deep learning. Particularly, we clarify: (1) Why do neural networks acquire generalization abilities? (2) Why do adversarial examples transfer between different models?. We provide a comprehensive set of experiments that support this new framework, as well as its underlying theory.