Abstract: Recently, generative adversarial networks (GAN) have made remarkable progress, particularly with the advent of Contrastive Language-Image Pretraining (CLIP), which take image and text into a ...

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Abstract: Anomaly detection (AD) identifies outliers for applications like defect and lesion detection. While CLIP shows promise for zero-shot AD tasks due to its strong generalization capabilities, ...

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Disclaimer: I'm redirecting efforts to pyglide and may be slow to address bugs here. I also recommend looking at @crowsonkb's v-diffusion-pytorch. See captions and more generations in the Gallery.

CLIP-LIT trained using only hundreds of unpaired images yields favorable results on unseen backlit images captured in various scenarios. 📖 For more visual results, go checkout our project page.

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