taesungp / contrastive-unpaired-translation

Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)

Home Page:https://taesung.me/ContrastiveUnpairedTranslation/

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

Some Questions and Comments

tom99763 opened this issue · comments

  1. Do you consider that instead of the feature map from CNN, using vector-quantized AE (VQVAE) for the future work? I think the result will be surprised due to its feature compression and sampleable properties for image-to-image translation task.

  2. It seems like the input-output pixel correlation largely impacts the translation result during early training process (multimodal translation or Animal-to-Human translation). Instead of predicting all at ones, two stage model (first contour, next texture) may improves the result.

Thank you

Hello, thanks for suggestions.

  1. I think incorporating VQVAE can be a good direction, particularly for saving compute.
  2. It may, especially if we go to higher resolution. But two-stage approaches are also more cumbersome to train.