SimCLR architecture
SimCLR stands for Simple Contrastive Learning Architecture. This architecture is based on the paper "A Simple Framework for Contrastive Learning of Visual Representations", published by Geoffrey Hinton and Google Team. Geoffrey Hinton (just like Yann LeCun) is a co-recipient of the Turing Award for his work on Deep Learning. There are SimCLR and SimCLR2 versions. SimCLR2 is a larger and denser network than SimCLR. At the time of writing, SimCLR2 was the best architecture update available, but don't be surprised if there is a SimCLR3 soon that is even denser and better than the previous one.
The architecture has shown in relation to the ImageNet dataset that we can achieve 93% accuracy with just 1% of labels. This is a truly remarkable result considering that it took over 2 years and a great deal of effort from over 140,000 labelers (mostly graduate students) on Mechanical Turk to label ImageNet by hand. It was a massive undertaking carried out...
