Zero-Shot Text-to-Image Generation

Published in

International Conference on Machine Learning(2021)

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TL;DR

This work describes a simple approach based on a transformer that autoregressively models the text and image tokens as a single stream of data that is competitive with previous domain-specific models when evaluated in a zero-shot fashion.

Abstract

Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion.

Authors

I. Sutskever

26 papers

Alec Radford

12 papers

Scott Gray

7 papers

References64 items

Generative Adversarial Networks

Learning Transferable Visual Models From Natural Language Supervision

On the Binding Problem in Artificial Neural Networks

Text-to-Image Generation Grounded by Fine-Grained User Attention

X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers

Zero-Shot Text-to-Image Generation

Published in

International Conference on Machine Learning(2021)

External Links:

Generate Graph

TL;DR

Abstract

Authors

I. Sutskever

26 papers

Alec Radford

12 papers

Scott Gray

7 papers

References64 items

Generative Adversarial Networks

Learning Transferable Visual Models From Natural Language Supervision

On the Binding Problem in Artificial Neural Networks

Text-to-Image Generation Grounded by Fine-Grained User Attention

X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers

Gabriel Goh

2 papers

Chelsea Voss

3 papers

Mikhail Pavlov

4 papers

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all of the all-reduces have ﬁnished and the global norm has been computed

Once all GPUs on a machine have ﬁnished steps (7) and (8) for every resblock in the model