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[P] Implementing Billion-scale semi-supervised learning for image classification using Pytorch

Hi, reddit !! We are Myeongjun Kim and Taehun Kim. Our major is computer vision research using deep learning. Previously, we implemented the paper with RandWireNN pytorch version and tensorflow version. This time, we implemented a paper on “Billion-scale semi-supervised learning for image classification written by Facebook AI”(https://arxiv.org/abs/1905.00546). To briefly describe the paper, it is stated that the classification performance is improved by using unlabeled data. We realized that it was a simple and novel idea. So, we implemented it.

Due to the lack of GPU resources and unlabeled data, we are delaying the experiment on ImageNet and are experimenting with CIFAR-100 first. We would like to ask for your interest and feedback.

Thank you for reading long long story.

Github URL: https://github.com/leaderj1001/Billion-scale-semi-supervised-learning

submitted by /u/leaderj1001
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.