Before revision:  The networks eventually break altogether when rote memorization becomes impossible such as when the intra-class variability exceeds their capacity. We further show that another type of feedforward network, called a relational network (RN), which was shown to successfully solve seemingly difficult visual question answering (VQA) problems on the CLEVR datasets, suffers similar limitations.
After revision:  The networks eventually break altogether when rote memorization becomes impossible such as when the intra-class variability exceeds network capacity.