TL;DR: We announced that we'd support the Reproducibility Challenge $500 per paper reproduced, and we're announcing the award winners for the Spring '21 edition, as well as our support for the Fall '21 edition of the challenge. Check out the awesome papers below---Hey HN! Creator of DagsHub here. We really care about reproducibility. That's why, a while back, we announced our support for the Papers with Code ML Reproducibility Challenge, and that we'd award participants $500 per paper reproduced (according to the guidelines), to align incentives and put our money where our mouth is!Today, I'm really happy to share the teams that were given the award, and the projects they worked on – read the full blog here:  honestly think the full read is interesting and worth your time, but here are the highlights from the papers:1. Contextual Decomposition Explanation Penalization (CDEP) – The original paper proposes a method to reduce the chance of models learning spurious correlations instead of the actually important features. The team that reproduced it re-implemented the original project in Tensorflow, rewriting some functions completely from scratch! Along the way, they made a contribution to the Tensorflow addons repo2. Self-supervision for Few-shot Learning – As its name suggests, this paper tests the importance of self-supervised learning in few-shot learning contexts. The team that reproduced it explored different input configurations than the one proposed in the article, and found out that it significantly affects the performance.3. GANSpace: Discovering Interpretable GAN Controls – A proposed method to use "simple" PCA to create controls for GANs that are more humanly interpretable while being more computationally efficient. The team re-implemented the original implementation in Tensorflow and trained the model with a few benchmark datasets, they have a lot of very cool examples of the method in their report.Thank you to everyone who took part in this challenge! None of this could be possible without you and we learned a lot in this process!So what's next – well we've decided to continue the support the Fall 2021 edition of the Reproducibility Challenge! We want to host more reproduced papers since this makes the ML field better for everyone.If you want to take part and move the field forward on the reproducibility front, check out the guidelines for more information on how to take part: 
