Contrastive Learning for Unpaired Image-to-Image Translation
Contrastive learning has provided a huge boost in self-supervised representation learning. This paper shows that this can even improve other self-supervised learning algorithms, like generative models in the GAN framework. I am really excited about how image to image translation networks can be used for domain similarity analysis. Thanks for watching! Please Subscribe!
Paper Links:
Contrastive Unpaired Translation (contains code, video, website, and paper link): https://github.com/taesungp/contrasti...
Contrastive Predictive Coding: https://arxiv.org/pdf/1905.09272.pdf
SinGAN: https://arxiv.org/pdf/1905.01164.pdf
EfficientDet: https://arxiv.org/pdf/1911.09070.pdf
Feature Pyramid Networks for Object Detection: https://arxiv.org/pdf/1612.03144.pdf
Don't Stop Pretraining: https://arxiv.org/pdf/2004.10964.pdf
CycleGAN: https://arxiv.org/pdf/1703.10593.pdf
SimCLR: https://arxiv.org/pdf/2002.05709.pdf
MoCo: https://arxiv.org/pdf/1911.05722.pdf
On the Measure of Intelligence: https://arxiv.org/pdf/1911.01547.pdf
Chapters
0:00 Beginning
1:37 Image-to-Image Translation
2:42 Example with Robots! (AVID)
3:26 High-level overview of algorithm
4:23 How Image Patches are Compared
6:53 PatchNCE Loss
7:52 MLP Projection Head
8:48 PatchNCE Loss (Equation)
10:52 External Negative test from Dataset rather than the Same Image
11:54 Final Objective
13:48 Ablation Takeaways
14:37 Results
15:38 Application to Domain Similarity
17:50 Interest in Domain Similarity Metrics
Watch video Contrastive Learning for Unpaired Image-to-Image Translation online without registration, duration hours minute second in high quality. This video was added by user Connor Shorten 04 August 2020, don't forget to share it with your friends and acquaintances, it has been viewed on our site 6,589 once and liked it 160 people.