Transfer Learning for Human & AI

High-qualitypixel-level annotation is still time-consuming and laborious — a bottleneck for several deep learning applications . Weshow that our approach can surpass the accuracy of state-of-the-art methods in .ground segmentation datasets: iCoSeg, DAVIS, and Rooftop . It achieves 91.5\% accuracy in a known semantic segmentation dataset, Cityscapes, being 74.75 times faster than the original annotation procedure . The appendixpresents additional qualitative results . Code and video demonstration will be released upon publication . The

Author(s) : Jordão Bragantini, Alexandre Falcão, Laurent Najman

Links : PDF - Abstract

Code :


Keywords : annotation - segmentation - accuracy - - qualitative -

• Hands-On Machine Learning with Scikit-Learn and TensorFlow
• Computational Linear Algebra by Rachel Thomas
• Complete Python Bootcamp
• Machine Learning Nanodegree
• Siraj Raval
• Deep Learning A-Z
• Introduction to Data Science in Python on Coursera
• Khan Academy Multivariable Calculus