@conference {2580,
	title = {One Shot Learning via Compositions of Meaningful Patches},
	booktitle = {International Conference on Computer Vision (ICCV)},
	year = {2015},
	abstract = {<p>The task of discriminating one object from another is al- most trivial for a human being. However, this task is compu- tationally taxing for most modern machine learning meth- ods; whereas, we perform this task at ease given very few examples for learning.\&nbsp; It has been proposed that the quick grasp of concept may come from the shared knowledge be- tween\&nbsp; the\&nbsp; new\&nbsp; example\&nbsp; and\&nbsp; examples\&nbsp; previously\&nbsp; learned. We believe that the key to one-shot learning is the sharing of common parts as each part holds immense amounts of in- formation on how a visual concept is constructed.\&nbsp; We pro- pose an unsupervised method for learning a compact dictio- nary of image patches representing meaningful components of an objects.\&nbsp; Using those patches as features, we build a compositional model that outperforms a number of popu- lar algorithms on a one-shot learning task. We demonstrate the effectiveness of this approach on hand-written digits and show that this model generalizes to multiple datasets.</p>
},
	author = {Alex Wong and Alan Yuille}
}
