Reviews: Neural Diffusion Distance for Image Segmentation

The paper is clearly written, everything proposed in the paper makes sense and seems like a natural thing to do (I had been working on the same problem, so I am entirely in favor of the pursued direction). This is not only important for giving credit to earlier works. A more crucial question in connection with these works, is whether the structured layer adds something on top of the ability of a cnn to compute embeddings for image segmentation. In experiments that I have been working on it has been really hard to beat a well-tuned, plain convnet trained with a siamese loss, and introducing a spectral normalization layer only added complications. It would be really useful if the authors could do this comparison on top of a strong baseline (e.g. the methods mentioned above) and indicate whether the resulting embeddings ( eigenvectors) are any better than those delivered from the original baselines.