text: Quality This paper demonstrates that convolutional and relational neural networks fail to solve visual relation problems by training networks on artificially generated visual relation data.  This points at important limitations of current neural network architectures where architectures depend mainly on rote memorization. Clarity The rationale in the paper is straightforward.  I do think that breakdown of networks by testing on increasing image variability is expected given that there is no reason that networks should generalize well to parts of input space that were never encountered before. Originality While others have pointed out limitations before,  this paper considers relational networks for the first time. Significance  This work demonstrates failures of relational networks on relational tasks,  which is an important message.  At the same time, no new architectures are presented to address these limitations. Pros Important message about network limitations. Cons Straightforward testing of network performance on specific visual relation tasks.  No new theory development.  Conclusions drawn by testing on out of sample data may not be completely valid.