​​SwinIR: Image Restoration Using Swin Transformer

Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy, and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers, which show impressive performance on high-level vision tasks.

The authors use a model SwinIR based on the Swin Transformers. Experimental results demonstrate that SwinIR outperforms state-of-the-art methods on different tasks (image super-resolution, image denoising, and JPEG compression artifact reduction) by up to 0.14~0.45dB, while the total number of parameters can be reduced by up to 67%.

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detailed overview

#deeplearning #transformer #computervision

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​​Summarizing Books with Human Feedback

OpenAI fine-tuned GPT3 to summarize books well enough to be human-readable. Main approach: recursively split text into parts and then meta-summarize summaries.

This is really important because once there will be a great summarization SOTA we won't need editors to write posts for you. And researchers ultimatively will have some asisstance interpreting models' results.

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ArXiV

#deeplearning #nlp #gpt #transformer

@pythonicAi