2017 Winter Project Week/OCM-MRI - Revision history

Frank at 15:41, 13 January 2017

2017-01-13T15:41:53Z

2017-01-10T17:04:29Z

2017-01-10T17:04:09Z

2017-01-10T17:03:35Z

2017-01-09T18:34:32Z

2017-01-09T18:33:36Z

2017-01-09T18:32:29Z

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 <!-- Progress and Next steps bullet points (fill out at the end of project week) -->
-* Identified Generative Adversarial Networks (GAN) and Variational Autoencoders as candidates
+* Discussions
 * In particular, Variational Autoencoders are able to model conditional distributions as required here
 |}

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 <!-- Progress and Next steps bullet points (fill out at the end of project week) -->
-* Identified Generative Adversarial Networks (GAN) and Variational Autoencoders as possible approach
+* Identified Generative Adversarial Networks (GAN) and Variational Autoencoders as candidates
 * In particular, Variational Autoencoders are able to model conditional distributions as required here
 |}

@@ Line 27: / Line 27: @@
 <!-- Progress and Next steps bullet points (fill out at the end of project week) -->
 * Identified Generative Adversarial Networks (GAN) and Variational Autoencoders as possible approach
 |}

@@ Line 26: / Line 26: @@
+|
 <!-- Progress and Next steps bullet points (fill out at the end of project week) -->
+* Identified Generative Adversarial Networks (GAN) and Variational Autoencoders as possible approach
 |}

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 <!-- Approach and Plan bullet points -->
 * Look into different software frameworks, choose one
-* Investigate generative deep neural networks techniques
+* Investigate generative deep neural network techniques
 * Produce preliminary results on hybrid US+MRI data
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