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@@ -23,7 +23,7 @@ While the adversarial network is clearly superior in terms of sharpness and cons <img src="https://github.com/dyelax/Adversarial_Video_Generation/raw/master/Results/Gifs/rainbow_NonAdv.gif" width="50%" /> -Using the error measurements outlined in the paper (Peak Signal to Noise Ratio and Sharp Difference) did not show significant difference between adversarial and non-adversarial training. I believe this is because sequential frames from the Ms. Pac-Man dataset have no motion in the majority of pixels. While I could not replicate the paper's results numerically, it is clear that adversarial training produces a qualitative improvement in the sharpness of the generated frames, especially over long time spans. You can view the loss and error statistics by running `tensorboard --logdir=./Results/Summaries/` from the root of this project. +Using the error measurements outlined in the paper (Peak Signal to Noise Ratio and Sharp Difference) did not show significant difference between adversarial and non-adversarial training. I believe this is because sequential frames from the Ms. Pac-Man dataset have no motion in the majority of pixels, while the original paper was trained on real-world video where there is motion in much of the frame. Despite this, it is clear that adversarial training produces a qualitative improvement in the sharpness of the generated frames, especially over long time spans. You can view the loss and error statistics by running `tensorboard --logdir=./Results/Summaries/` from the root of this project. ## Usage |