Comments (4)
Hi,
we use all 13,320 videos for training without the split.
Thanks!
from mocogan-hd.
Thank you for the clarification!
from mocogan-hd.
Hello! I have one more question.
You compared with baselines which mostly uses "train" split for the generator for the evaluation. However, you use all data for training; isn't it unfair comparison?
Sincerely,
Sihyun.
from mocogan-hd.
Hi Sihyun,
We follow the settings of DVD-GAN, the strongest baseline prior to our work.
The main drawback of the UCF-101 dataset is the relatively poor image generation quality (>40 FID), our video synthesis quality (FVD) is highly correlated to the image generation when the FID is not good enough. To be honest, we are not sure if using the training split can improve the FID or not. It is interesting to perform an experiment on this.
Thanks!
from mocogan-hd.
Related Issues (17)
- Question about the cross-domain video discriminator HOT 1
- Augmentation for training? HOT 1
- Question about Inception score evaluation HOT 1
- Why feed real data into the video discriminator when training G? HOT 1
- About the evaluation code HOT 3
- Question about the way you finetune the generator HOT 1
- How to train on a custom dataset? HOT 2
- Cannot run pca_stats.py HOT 2
- Inference issue using pre-trained models
- how to compute similarity loss in equation (3)? HOT 1
- Incorrect link for the image generator checkpoint on FaceForensics HOT 2
- Hyperparameters to train StyleGANv2 on UCF-101 HOT 2
- README should be updated HOT 1
- Question about the FVD evaluation HOT 6
- Did you use any truncation or curation for the released samples? HOT 7
- Did you cut first seconds of the FaceForensics dataset? HOT 2
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