ICLR 2020 (International Conference on Learning Representations) papers and their codes. Papers ranked based on stars

PyRetri: An open source deep learning based unsupervised image retrieval toolbox built on PyTorch🔥

Content-based Image retrieval

Paper: https://arxiv.org/abs/2005.02154

Github: https://github.com/PyRetri/PyRetri

Installation: https://github.com/PyRetri/PyRetri/blob/master/docs/INSTALL.md

Short over view of Artificial Neural Networks with examples
Page:
https://www.infinitycodex.in/

Behind the Scene: Revealing the Secrets of
Pre-trained Vision-and-Language Models

Paper:
https://arxiv.org/pdf/2005.07310.pdf

Related Codes:

https://github.com/airsplay/lxmert

https://github.com/ChenRocks/UNITER

You Can Color Your Grandparent's Old Pictures or Videos with AI DeOldify Tool

Github link of "AI DeOldify Tool":

https://github.com/jantic/DeOldify?fbclid=IwAR2-glzM1UYuWHJWuNKi741bm8aeofZdE1-0v9ZN-6Lmlmh4ta63mn5ydZc


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Masked face recognition dataset👇

Wolrd’s most complete Masked Face Recognition Dataset is Free to Download:

https://medium.com/the-programming-hub/wolrds-most-complete-masked-face-recognition-dataset-is-for-free-10d780eed512

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It's CVPR time!
We will not meet in person
next week at CVPR 2020 Seattle:
The conference has gone virtual...

But, You can follow what happens there almost in real time: fill below link and receive every day during CVPR the official magazine CVPR Daily (16-17-18 June) - with all the highlights from CVPR, the Computer Vision and Pattern Recognition conference.

https://www.rsipvision.com/feel-at-cvpr-as-if-you-were-at-cvpr/

Open Access version of papers are available at:
http://openaccess.thecvf.com/CVPR2020.py

List some of the free Artificial Intelligence courses that come from Harvard University, MIT University, and Stanford University that anyone can attend, no matter where you live

One more great website specialized to AI with News, Articles, Opinions, Tutorials, Resources and much more supported by Geeks of AI

Recently published Comprehensive survey about role of Deep Learning for Scientific discovery (March, 2020). Well structured information given from the authors by providing supplementary materials (Github code links).
It worth to spend time to read.

Up-to-date and detailed explanation of Deep Learning Models from Sebastian Raschka
A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks. (80 Jupyter Notebook notes in total)