Title Unsupervised and transfer learning challenge
Publication TypeConference Paper
Year of Publication2011
AuthorsGuyon, I, Dror, G, Lemaire, V, Taylor, G, Aha, DW
Conference NameInternational Joint Conference on Neural Networks
PublisherIEEE Press
Conference LocationSan Jose, CA
Keywordscompetition, deep learning, machine learning, Transfer learning, unsupervised learning

We organized a data mining challenge in “unsupervised and transfer learning” (the UTL challenge), in collaboration with the DARPA Deep Learning program. The goal of this year’s challenge was to learn good data representations that can be re-used across tasks by building models that capture regularities of the input space. The representations provided by the participants were evaluated by the organizers on supervised learning “target tasks”, which were unknown to the participants. In a first phase of the challenge, the competitors were given only unlabeled data to learn their data representation. In a second phase of the challenge, the competitors were also provided with a limited amount of labeled data from “source tasks”, distinct from the “target tasks”. We made available large datasets from various application domains: handwriting recognition, image recognition, video processing, text processing, and ecology. The results indicate that learned data representation yield results significantly better than what can be achieved with raw data or data preprocessed with standard normalizations and functional transforms. The UTL challenge is part of the IJCNN 2011 competition program. The website of the challenge remains open for submission of new methods beyond the termination of the challenge as a resource for students and researchers.

Refereed DesignationRefereed
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machine learning
deep learning
unsupervised learning
transfer learning