Practicum: (Energy-based) Generative Adversarial Networks

Practicum: (Energy-based) Generative Adversarial Networks

During this week’s practicum, we explored Generative Adversarial Networks (GANs) and how they can produce realistic generative models. We then compared GANs with VAEs from week 8 to highlight key differences between two networks. Next, we discussed several model limitations of GANs. Finally, we looked at the source code for the PyTorch example Deep Convolutional Generative Adversarial Networks (DCGAN).

Week 9 – Practicum: (Energy-based) Generative adversarial networks

0:00:00 – Week 9 – Practicum

PRACTICUM: http://bit.ly/pDL-en-09-3 During this week’s practicum, we explored Generative Adversarial Networks (GANs) and how they can produce realistic generative models. We then compared GANs with VAEs from week 8 to highlight key differences between two networks. Next, we discussed several model limitations of GANs. Finally, we looked at the source code for the PyTorch example Deep Convolutional Generative Adversarial Networks (DCGAN). 0:00:57 – Intro to GANs 0:30:44 – Difference between GANs and VAEs and major pitfalls in GANs 0:48:31 – DCGAN source code

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