text: This paper explores learning dynamic filters for CNNs.  The filters are generated by using the features of an autoencoder on the input image, and linearly combining a set of base filters for each layer.  This addresses an interesting problem which has been looked at a lot before, but with some small new parts.  There is a lot of prior work in this area  that should be cited in the area of dynamic filters and steerable filters.  There are also parallels to ladder networks that should be highlighted.  The results indicate improvement over baselines,  however baselines are not strong baselines.  A key question is what happens when this method is combined with VGG11 which the authors train as a baseline?  What is the effect of the reconstruction loss?  Can it be removed?  There should be some ablation study here. Figure 5 is unclear what is being displayed,  there are no labels. Overall I would advise the authors to address these questions and suggest this as a paper suitable for a workshop submission.