Visual Domain Adaptation in the Deep Learning Era

by Timothy M. Hospedales, Mathieu Salzmann, Gabriela Csurka

Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance. While in many situations huge volumes of unlabeled data can be and often are generated and available, the cost of acquiring data labels remains high. Transfer learning (TL), and in particular domain adaptation (DA), has emerged as an effective solution to overcome the burden of annotation, exploiting the unlabeled data...

• Format: Paperback
• Language: English
• ISBN: 3031791703
• ISBN13: 9783031791703
• Release Date: April 2022
• Publisher: Springer
• Length: 168 Pages
• Weight: 0.75 lbs.
• Dimensions: 0.4" x 7.5" x 9.3"