[D] Influential papers round-up 2022. What are your favorites?

This page summarizes the projects mentioned and recommended in the original post on /r/MachineLearning

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  • stable-diffusion

    A latent text-to-image diffusion model

    StableDiffusion/Latent Diffusion Model. Before ChatGPT became the state of the show, it was not too long ago since Stable Diffusion was all over the internet and social media. Stable Diffusion is based on the paper High-Resolution Image Synthesis with Latent Diffusion Models, which was uploaded in December 2021. But since it was presented at CVPR 2022 and got the spotlight with the Stable Diffusion in August 2022, I think it's fair to include it in this 2022 list.

  • Swin-Transformer

    This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

    ConvNeXt. The A ConvNet for the 2020s paper is a highlight for me because the authors were able to design a purely convolutional architecture that outperformed popular vision transformers such as Swin Transformer (and all convolutional neural networks that came before it, of course).

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  • ConvNeXt

    Code release for ConvNeXt model

    Found relevant code at https://github.com/facebookresearch/ConvNeXt + all code implementations here

  • latent-diffusion

    High-Resolution Image Synthesis with Latent Diffusion Models

    Found relevant code at https://github.com/CompVis/latent-diffusion + all code implementations here

  • tabular-dl-pretrain-objectives

    Revisiting Pretrarining Objectives for Tabular Deep Learning

    Found relevant code at https://github.com/puhsu/tabular-dl-pretrain-objectives + all code implementations here

  • Sonar

    Write Clean Python Code. Always.. Sonar helps you commit clean code every time. With over 225 unique rules to find Python bugs, code smells & vulnerabilities, Sonar finds the issues while you focus on the work.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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