cot -gan
COT-GAN: Generating Sequential Data via Causal Optimal Transport: Supplementary material A Specifics on regularized Causal Optimal Transport
In Figure 11, we show a comparison of real and generated samples for human action sequences.
the related discussions and further experiment results in the new version, shall our paper be accepted
We thank all reviewers for the insightful feedback. Below we address all questions raised in the reviews. More intuition can be added in Section 3. COT could greatly benefit sequential learning. To support our intuition, we provide two arguments in Appendix A.3: the For the justification, please see our response to Reviewer 2. WaveGAN (trained with WGAN-GP loss) and COT -GAN without the mixing trick. We respectfully disagree with the reviewer on this comment.
the related discussions and further experiment results in the new version, shall our paper be accepted
We thank all reviewers for the insightful feedback. Below we address all questions raised in the reviews. More intuition can be added in Section 3. COT could greatly benefit sequential learning. To support our intuition, we provide two arguments in Appendix A.3: the For the justification, please see our response to Reviewer 2. WaveGAN (trained with WGAN-GP loss) and COT -GAN without the mixing trick. We respectfully disagree with the reviewer on this comment.