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dyelax avatar dyelax commented on June 17, 2024

@ssulun16 You are totally right. Cant believe I missed that.

From section 2.2 in the paper:

The discriminative model D takes a sequence of frames, and is trained to predict the probability that the last frames of the sequence are generated by G. Note only the last frames are either real of generated by G, the rest of the sequence is always from the dataset. This allows the discriminative model to make use of temporal information, so that G learns to produce sequences that are temporally coherent with its input.

I'm pretty busy with other projects right now, but would be awesome if you could make a PR that fixes this! Seems like it should just be a small tweak in the build_feed_dict function of DiscriminatorModel

from adversarial_video_generation.

serkansulun avatar serkansulun commented on June 17, 2024

I made some other changes before so the code isn't very clean but I'll do that if I can find some time after I have a final version for myself. In the meantime I'll open issues if I find other problems so that it can help other people.

from adversarial_video_generation.

serkansulun avatar serkansulun commented on June 17, 2024

I have fixed it but I also switched to Pytorch in the meantime, it is available in my repositories if anyone needs it.

from adversarial_video_generation.

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