Ian Goodfellow, Research Scientist OpenAI : Generative Adversarial Networks (GANs) #AIWTB 2016
Ian Goodfellow, Research Scientist at OpenAI takes us through Generative Adversarial Networks at AI With The Best online developer conference September 2016 #AIWTB.
Followed by a super Q&A session
Q&A
28:40 Can you explain the problem about GANs not knowing how to place body parts and textures within the image net - what are some solutions to those problems?
30:45 Specifically how can you place body parts and place 3D texture?
31:30 What are the commercial applications of GANs?
33:04 Where do you see GANs being applied outside of image related tasks?
33:13 How can you generate feedback loops in a GAN so it generates an output similar to the input in its dimensions to predict the next step in a time series ie. a video sequence like in the case of WaveNet?
33:34 What do you predict it will take to get the output to scale to a much larger output dimension?
34:16 Can GANs be used for Data Compression?
35:20 What is your goal with GANs?
Ian Goodfellow, Research Scientist at OpenAI takes us through Generative Adversarial Networks at AI With The Best online developer conference September 2016 #AIWTB.
Followed by a super Q&A session
Q&A
28:40 Can you explain the problem about GANs not knowing how to place body parts and textures within the image net – what are some solutions to those problems?
30:45 Specifically how can you place body parts and place 3D texture?
31:30 What are the commercial applications of GANs?
33:04 Where do you see GANs being applied outside of image related tasks?
33:13 How can you generate feedback loops in a GAN so it generates an output similar to the input in its dimensions to predict the next step in a time series ie. a video sequence like in the case of WaveNet?
33:34 What do you predict it will take to get the output to scale to a much larger output dimension?
34:16 Can GANs be used for Data Compression?
35:20 What is your goal with GANs?
