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.

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.