Computer Vision Methods Utilizing Interclass Transfer
Bart, E. and Ullman, S. (2005) Cross-generalization: Learning Novel Classes from a Single Example by Feature Replacement, CVPR, 2005.
Fei-Fei, L., Fergus, R. and Perona, P. (2004) Learning Generative Visual Models from Few Training Examples. CVPR04 Workshop on Generative-Model Based Vision, 2004.
Ferencz, A., Learned-Miller, E. and Malik J. (2005) Building a Classification Cascade for Visual Identification from One Example. ICCV, 2005.
Fink, M. (2004) Object Classification from a Single Example Utilizing Class Relevance Pseudo-Metrics. NIPS 2004
Krempp, S., Geman, D. and Amit, Y. (2002) Sequential Learning of Reusable Parts for Object Detection. Technical report, CS Johns Hopkins, 2002.
Miller, E., Matsakis, N. and Viola, P. Learning from One Example through Shared Densities on Transforms. CVPR 2000.
Torralba, A., Murphy, K. and Freeman, W. (2004) Sharing Features: Efficient Boosting Procedures for Multiclass Object Detection. CVPR 2004.
Cognitive Evidence for Interclass Transfer
Kemp, C. S., Perfors, A., and Tenenbaum, J. B. (2004) Learning Domain Structures Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society 2004
Machine Learning Formalizations of Interclass Transfer
Thrun, S. (1996) Is Learning The n-th Thing Any Easier Than Learning The First. NIPS 1996.
Thrun, S. and Pratt, L. (1997) Learning to Learn. Kluwer Academic Publishers, 1997.
Ben-David, S., and Schuller, R. (2004) Exploiting Task Relatedness for Multitask Learning. COLT 2003.
Interclass Transfer: why learning to recognize many objects is easier than learning to recognize just one
