1 research outputs found
A Classification approach towards Unsupervised Learning of Visual Representations
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