  Deep neural networks (DNNs) can easily fit a random labeling of the training
data with zero training error. What is the difference between DNNs trained with
random labels and the ones trained with true labels? Our paper answers this
question with two contributions. First, we study the memorization properties of
DNNs. Our empirical experiments shed light on how DNNs prioritize the learning
of simple input patterns. In the second part, we propose to measure the
similarity between what different DNNs have learned and memorized. With the
proposed approach, we analyze and compare DNNs trained on data with true labels
and random labels. The analysis shows that DNNs have \textit{One way to Learn}
and \textit{N ways to Memorize}. We also use gradient information to gain an
understanding of the analysis results.
