Web: http://arxiv.org/abs/2201.12078

June 16, 2022, 1:11 a.m. | Junlin Han, Pengfei Fang, Weihao Li, Jie Hong, Mohammad Ali Armin, Ian Reid, Lars Petersson, Hongdong Li

cs.LG updates on arXiv.org arxiv.org

We present You Only Cut Once (YOCO) for performing data augmentations. YOCO
cuts one image into two pieces and performs data augmentations individually
within each piece. Applying YOCO improves the diversity of the augmentation per
sample and encourages neural networks to recognize objects from partial
information. YOCO enjoys the properties of parameter-free, easy usage, and
boosting almost all augmentations for free. Thorough experiments are conducted
to evaluate its effectiveness. We first demonstrate that YOCO can be seamlessly
applied to varying …

arxiv augmentation boosting cv data

More from arxiv.org / cs.LG 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