DALL-E: Text-to-Image generation - Explained!

In this video, we take a look at a DALL-E for text-to-image generation. What is it? Why do we have it? How does it look? ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ RESOURCES [1 📚] Slides: https://link.excalidraw.com/p/readonly/NXtiUh19HjH4BuC2IQ6V [2 📚] DALL-E main paper: https://arxiv.org/pdf/2102.12092 [3 📚] DALL-E blog page: https://openai.com/index/dall-e/ [4 📚] Evolution of auto encoders: https://youtu.be/XyWNmHZi1oA?si=0X5iE2FKfToDaRNM [5 📚] Colab notebook I put together to understand the gumbel distribution, gumbel max trick and Gumbel Softmax Relaxation: https://colab.research.google.com/drive/1KSKB3AIUzyMnpym8HeSVZCxOtzS-DI9u#scrollTo=1af4a395 [6 📚] Nice mathematical proof to show gumbel max trick: [https://github.com/priyammaz/PyTorch-Adventures/blob/main/PyTorch for Generation/AutoEncoders/Intro to AutoEncoders/gumbel_softmax_quantizer.ipynb](https://github.com/priyammaz/PyTorch-Adventures/blob/main/PyTorch%20for%20Generation/AutoEncoders/Intro%20to%20AutoEncoders/gumbel_softmax_quantizer.ipynb) [7 📚] Attention is all you need paper: https://arxiv.org/pdf/1706.03762 [8 📚] Image is worth 16 x 16 words paper: https://arxiv.org/pdf/2010.11929 [9 📚] Improving generative language understanding paper: https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf [10 📚] Learning Bounded Context-Free-Grammar via LSTM and the Transformer: Difference and Explanations paper: https://arxiv.org/pdf/2112.09174 [11 📚] DALL-E architecture code: https://github.com/openai/DALL-E/blob/master/dall_e/encoder.py PLAYLISTS FROM MY CHANNEL ⭕ Reinforcement Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd9kS--NgVz0EPNyEmygV1Ha&si=AuThDZJwG19cgTA8 Natural Language Processing: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA