Deep Learning Deep Dive: Recent Episodes

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The actual paper for DALL-E was released only a few days after we published our episode 2, so in this episode we re-visit DALL-E in its full published glory. Joining us as a special guest for this episode is Aditya Ramesh from OpenAI, the lead author of DALL-E.

DALL-E blog post: https://openai.com/blog/dall-e/

DALL-E paper: https://arxiv.org/abs/2102.12092

DALL-E code (encoder/decoder model only, so far): https://github.com/openai/dall-e

Deep Learning Deep Dive is also available on YouTube, where we scroll through relevant parts of the paper and code while talking about them:

https://www.youtube.com/watch?v=PtdpWC7Sr98

We reached out and collected written consent from all participating audience speakers.

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Andrej Karpathy and Justin Johnson deep dive into OpenAI's DALL-E and use it as an anchor point to recurse into some of the recent work in AI on image generation. Approximate agenda:

DALL-E Blog Post:

https://openai.com/blog/dall-e/

ImageGPT

https://openai.com/blog/image-gpt/

VQ-VAE

https://arxiv.org/abs/1711.00937

VQ-VAE-2

https://arxiv.org/abs/1906.00446

Gumbel-Softmax / Concrete Distribution

https://arxiv.org/abs/1611.01144

https://arxiv.org/abs/1611.00712

VQGAN

https://arxiv.org/abs/2012.09841

Andrej's attempted re-implementation of VQVAE and GumbelSoftmax:

https://github.com/karpathy/deep-vector-quantization/blob/main/model.py

You can see a video version of this episode on YouTube:

https://www.youtube.com/watch?v=gMc90bqHMSM

We reached out to all speakers and obtained their written consent to appear in this recording.