A Classification approach towards Unsupervised Learning of Visual Representations
Abstract
In this paper, we present a technique for unsupervised learning of visual representations. Specifically, we train a model for foreground and background classification task, in the process of which it learns visual representations. Foreground and background patches for training come af- ter mining for such patches from hundreds and thousands of unlabelled videos available on the web which we ex- tract using a proposed patch extraction algorithm. With- out using any supervision, with just using 150, 000 unla- belled videos and the PASCAL VOC 2007 dataset, we train a object recognition model that achieves 45.3 mAP which is close to the best performing unsupervised feature learn- ing technique whereas better than many other proposed al- gorithms. The code for patch extraction is implemented in Matlab and available open source at the following link .
Keywords
Cite
@article{arxiv.1806.00428,
title = {A Classification approach towards Unsupervised Learning of Visual Representations},
author = {Aditya Vora},
journal= {arXiv preprint arXiv:1806.00428},
year = {2018}
}