%0 Conference Paper %1 Quattoni2008Transfer %A Quattoni, Ariadna %A Collins, Michael %A Darrell, Trevor %B Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on %D 2008 %K active image labeling learning phd unread %P 1-8 %R 10.1109/CVPR.2008.4587637 %T Transfer learning for image classification with sparse prototype representations %X To learn a new visual category from few examples, prior knowledge from unlabeled data as well as previous related categories may be useful. We develop a new method for transfer learning which exploits available unlabeled data and an arbitrary kernel function; we form a representation based on kernel distances to a large set of unlabeled data points. To transfer knowledge from previous related problems we observe that a category might be learnable using only a small subset of reference prototypes. Related problems may share a significant number of relevant prototypes; we find such a concise representation by performing a joint loss minimization over the training sets of related problems with a shared regularization penalty that minimizes the total number of prototypes involved in the approximation. This optimization problem can be formulated as a linear program that can be solved efficiently. We conduct experiments on a news-topic prediction task where the goal is to predict whether an image belongs to a particular news topic. Our results show that when only few examples are available for training a target topic, leveraging knowledge learnt from other topics can significantly improve performance.

@inproceedings{Quattoni2008Transfer, abstract = {To learn a new visual category from few examples, prior knowledge from unlabeled data as well as previous related categories may be useful. We develop a new method for transfer learning which exploits available unlabeled data and an arbitrary kernel function; we form a representation based on kernel distances to a large set of unlabeled data points. To transfer knowledge from previous related problems we observe that a category might be learnable using only a small subset of reference prototypes. Related problems may share a significant number of relevant prototypes; we find such a concise representation by performing a joint loss minimization over the training sets of related problems with a shared regularization penalty that minimizes the total number of prototypes involved in the approximation. This optimization problem can be formulated as a linear program that can be solved efficiently. We conduct experiments on a news-topic prediction task where the goal is to predict whether an image belongs to a particular news topic. Our results show that when only few examples are available for training a target topic, leveraging knowledge learnt from other topics can significantly improve performance.}, added-at = {2009-07-13T09:30:06.000+0200}, author = {Quattoni, Ariadna and Collins, Michael and Darrell, Trevor}, biburl = {https://www.bibsonomy.org/bibtex/234a82960b4389690f692326f80e2689f/casi}, booktitle = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on}, description = {Training with unlabelled data.}, doi = {10.1109/CVPR.2008.4587637}, interhash = {76a3a4e933d1e585d03b717b8e2acae1}, intrahash = {34a82960b4389690f692326f80e2689f}, issn = {1063-6919}, keywords = {active image labeling learning phd unread}, month = {June}, pages = {1-8}, timestamp = {2010-01-29T16:03:41.000+0100}, title = {Transfer learning for image classification with sparse prototype representations}, year = 2008 }