Batch Region Norm or Instance Region Norm? about rn HOT 4 CLOSED

Hi yutao,

Thanks for sharing your inspiring work. I have a question about the specific implementation of Region Norm. As you've mentioned in your paper, Region Norm should be a generation to Instance Norm. However, from your implementation, rn is still based on the distribution of mask/unmask regions in the whole batch. Is there some errors in your implementation, or you've found that performance based on instance regions worse than batch regions.

Hi, thanks for your comments. It's exactly what we are going to make a statement in the future application of RN.
First, sorry that we didn't discuss this in the AAAI2020 paper. RN wants to bring an insight that spatially region-wise normalization is better for some CV tasks such as inpainting. Theoretically, RN can be both BN-style or IN-style. Both have pros and cons. IN-style RN gives less blurring results and achieves style consistence to background in some extent, while suffers from spatial inconsistence if the model representation ability is limited. BN-style RN gives higher PSNR on an aligned validation data, but makes regions more blurring and causes much data-bias risk when testing data distribution has a certain shift to training data distribution. One chooses the RN style according to the specific scene.
Thanks.

from rn.

from rn.

the generated region is not well blended in background regions.

1. " the generated region is not well blended in background regions": I think there are many possible reasons such as data distribution shift. You can also re-train RN on ImageNet. Maybe the results be better.
2. I didn't try removing the gamma. But pls share the results on it to me if you gonna do it.

Feel free to ask me if you still have questions.

Best

from rn.

Look forward to future discussing.

Best

from rn.