Web: http://arxiv.org/abs/2206.04170

June 24, 2022, 1:12 a.m. | Pranav Singh, Elena Sizikova, Jacopo Cirrone

cs.CV updates on arXiv.org arxiv.org

Recent advances in Deep Learning and Computer Vision have alleviated many of
the bottlenecks, allowing algorithms to be label-free with better performance.
Specifically, Transformers provide a global perspective of the image, which
Convolutional Neural Networks (CNN) lack by design. Here we present Cross
Architectural Self-Supervision, a novel self-supervised learning approach which
leverages transformers and CNN simultaneously, while also being computationally
accessible to general practitioners via easily available cloud services.
Compared to existing state-of-the-art self-supervised learning approaches, we
empirically show CASS …

analysis arxiv cross cv image medical

More from arxiv.org / cs.CV updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY