@conference {2580, title = {One Shot Learning via Compositions of Meaningful Patches}, booktitle = {International Conference on Computer Vision (ICCV)}, year = {2015}, abstract = {

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.\  It has been proposed that the quick grasp of concept may come from the shared knowledge be- tween\  the\  new\  example\  and\  examples\  previously\  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.\  We pro- pose an unsupervised method for learning a compact dictio- nary of image patches representing meaningful components of an objects.\  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.

}, author = {Alex Wong and Alan Yuille} }