​​Image GPT
by openai

The authors have shown that by trading off 2-D knowledge for scale and by choosing predictive features from the middle of the network, a sequence transformer can be competitive with top convolutional nets for unsupervised image classification.
Notably, they achieved their results by directly applying the GPT-2 language model to image generation. Their results suggest that due to its simplicity and generality, a sequence transformer given sufficient compute might ultimately be an effective way to learn excellent features in many domains.

There are two methods they use to assess model performance:
[0] linear probe, uses the trained model to extract features from the images in the downstream dataset and then fits a logistic regression to the labels
[1] fine-tunes the entire model on the downstream dataset :youknow:


blog: https://openai.com/blog/image-gpt/
papers:
icml 2020 (v1)
(v2)
github (code is provided as-is, no updates expected): https://github.com/openai/image-gpt

#openai #gpt2 #language #image #icml2020

​​AI Generated Pokemon Sprites with GPT-2

Author trained #GPT2 model to generate #pokemon sprites, encoding them as the lines of characters (including color). Surprisingly, results were decent, so this leaves us wonder if #GPT3 results would be better.

YouTube: https://www.youtube.com/watch?v=Z9K3cwSL6uM
GitHub: https://github.com/MatthewRayfield/pokemon-gpt-2
Article: https://matthewrayfield.com/articles/ai-generated-pokemon-sprites-with-gpt-2/
Example: https://matthewrayfield.com/projects/ai-pokemon/

#NLU #NLP #generation #neuralart

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