  In this paper, we studied two identically-trained neural networks (i.e.
networks with the same architecture, trained on the same dataset using the same
algorithm, but with different initialization) and found that their outputs
discrepancy on the training dataset exhibits a "double descent" phenomenon. We
demonstrated through extensive experiments across various tasks, datasets, and
network architectures that this phenomenon is prevalent. Leveraging this
phenomenon, we proposed a new early stopping criterion and developed a new
method for data quality assessment. Our results show that a phenomenon-driven
approach can benefit deep learning research both in theoretical understanding
and practical applications.
