3. T. Darrell, A. Quattoni, M. Collins: Multi-task learning of Predictive Structures for Visual Category Learning

The transfer of knowledge from previously learned classes to novel tasks is a core challenge in large scale visual category learning. By exploiting predictive structures found in previous tasks, which presumably have relatively large amounts of training data, performance can be improved on novel problems with sparsely labeled data. We explore the learning of predictive structures based both on experience from the previous categories, and on so-called auxiliary problems constructed to provide additional tasks [Ando and Zhang 2005]. We show that such techniques can improve the performance of an image category classifier trained with just a labeled few examples.