Generation of a synthetic microbial dataset with deep learning style transfer

Efficient strategy to generate annotated synthetic dataset for training deep learning detectors

Written by Jarosław Pawłowski and Sylwia Majchrowska.

Deep learning models achieve considerably higher accuracy than traditional computer vision algorithms. When working on image recognition using traditional methods, feature extraction algorithms are tuned by hand which in many cases is a time-consuming procedure. On the contrary, in deep convolutional networks feature engineering is performed automatically — the network learns how to extract the best feature …

dataset deep learning generation learning object-detection object-segmentation style transfer synthetic-data-generation transfer