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authorCameron <cysmith1010@gmail.com>2016-10-25 19:34:30 -0600
committerGitHub <noreply@github.com>2016-10-25 19:34:30 -0600
commit5be358d54b8f2ee16294d4e90121ae0d502031eb (patch)
tree5eb356c47ce60705c852a905d2b1d0b0b357742f
parent55ccbae4dfc96d2a9c3db36313c4af4bb8a42e5f (diff)
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@@ -160,7 +160,7 @@ Animations can be rendered by applying the algorithm to each source frame. For
</p>
### Gradient Descent Initialization
-The initialization of the gradient descent is controlled using `--init_img_type` for single images and `--init_frame_type` or `--init_first_frame_type` for video frames. For single images, the gradient descent can be initialized with white noise, the content image, or the style image. For video frames, the gradient descent can also be initialized with the previous stylized frame or the previous stylized frame warped to the current frame. White noise allows an arbitrary number of distinct images to be generated. Whereas, initializing with a fixed image always converges to the same output.
+The initialization of the gradient descent is controlled using `--init_img_type` for single images and `--init_frame_type` or `--first_frame_type` for video frames. For single images, the gradient descent can be initialized with white noise, the content image, or the style image. For video frames, the gradient descent can also be initialized with the previous stylized frame or the previous stylized frame warped to the current frame. White noise allows an arbitrary number of distinct images to be generated. Whereas, initializing with a fixed image always converges to the same output.
Here we reproduce Figure 6 from the first paper:
<p align="center">