Transfer learning for image classification with sparse prototype representations

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...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Ariadna Quattoni, Michael Collins, Trevor Darrell
• Keywords: Arbitrary Kernel Function, Available Unlabeled Data, Computer Vision, CVPR 2008, Relevant Prototypes, Unlabeled Data, Unlabeled Data Points