Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: The paper proposes an unsupervised learning approach to image matching. The authors train a deep network for video frame interpolation, and use the trained model to infer the correspondences between frames with backpropagation-based sensitivity analysis. The authors show that adversarial training of the interpolation network improves the accuracy of the predicted matches.

This general approach to learning to match images has been introduced by (Long et al., ECCV 2016). The contribution of the paper is in adding adversarial loss to the method and showing it improves the quality of the predicted matches.

The paper is written clearly, contains novel and fairly interesting results.

Pros:
- The fact that adversarial training on image interpolation indirectly improves the quality of the matches (~10% relative improvement in accuracy@5, ~20% relative decrease in EPE) is interesting.
- The method is using a somewhat non-standard GAN formulation based on EbGAN. It is not clear if this formulation is advantageous, though
- The method is compared to relevant baselines

Cons:
- Limited novelty: "take an existing method and add a GAN" is not a very original approach
- The results of the method are on par with (Long et al., ECCV 2016) and worse than another unsupervised method by (Yu et al., arxiv 2016)

I am not sure how to calibrate my score for the workshop track, so please take the rating with a grain of salt. This is not a bad paper, but I don't see "very novel ideas" in it.
review_title: Official review

final_score: rating: 6: Marginally above acceptance threshold
review_content: The paper proposes a GAN architecture that given frames t,t+2 interpolates to find t+1, building upon the method of Long et al for optical flow estimation through frame interpolation, by adding a discriminator to the output image. However, it does not compare against Long et al., so we do not know at the end, if adding the adversarial network helps. If the authors could clarify that, it would be important for the paper. My other note would be for them to provide one paragraph describing the method of Long et al a bit more in detail, as now someone needs to read Long's paper to get the full picture.
review_title: null

Decision: Reject