Analysis of the IJCNN 2011 UTL challenge

We organized a challenge in “Unsupervised and Transfer Learning”: the UTL challenge (http://clopinet.com/ul). We made available large datasets from various application domains: handwriting recognition, image recognition, video processing, text processing, and ecology. The goal was to learn data repr...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Neural networks 2012-08, Vol.32, p.174-178
Hauptverfasser: Guyon, Isabelle, Dror, Gideon, Lemaire, Vincent, Silver, Daniel L., Taylor, Graham, Aha, David W.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:We organized a challenge in “Unsupervised and Transfer Learning”: the UTL challenge (http://clopinet.com/ul). We made available large datasets from various application domains: handwriting recognition, image recognition, video processing, text processing, and ecology. The goal was to learn data representations that capture regularities of an input space for re-use across tasks. The representations were evaluated on supervised learning “target tasks” unknown to the participants. The first phase of the challenge was dedicated to “unsupervised transfer learning” (the competitors were given only unlabeled data). The second phase was dedicated to “cross-task transfer learning” (the competitors were provided with a limited amount of labeled data from “source tasks”, distinct from the “target tasks”). The analysis indicates that learned data representations yield significantly better results than those obtained with original data or data preprocessed with standard normalizations and functional transforms.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2012.02.010