Unifying Multi-domain Multitask Learning: Tensor and Neural Network Perspectives

• Published in: Domain Adaptation in Computer Vision Applications pp. 291 - 309
• Main Authors: Yang, Yongxin, Hospedales, Timothy M.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Advances in Computer Vision and Pattern Recognition
• Subjects: Artificial intelligence; Image processing; Public administration
• ISBN: 3319583468; 9783319583464
• ISSN: 2191-6586; 2191-6594
• DOI: 10.1007/978-3-319-58347-1_16

Multi-domain learning aims to benefit from simultaneously learning across several different but related domains. In this chapter, we propose a single framework that unifies multi-domain learning (MDL) and the related but better studied area of multitask learningMulti-task learning (MTL). By exploiting the concept of a semantic descriptor we show how our framework encompasses various classic and recent MDL/MTL algorithms as special cases with different semantic descriptor encodings. As a second contribution, we present a higher order generalization of this framework, capable of simultaneous multitask-multi-domain learning. This generalization has two mathematically equivalent views in multilinear algebra and gated neural networks, respectively. Moreover, by exploiting the semantic descriptor, it provides neural networks the capability of zero-shotZero-shot learning learning (ZSL), where a classifier is generated for an unseen class without any training data; as well as zero-shot domain adaptationZero-shot domain adaptation (ZSDA), where a model is generated for an unseen domain without any training data. In practice, this framework provides a powerful yet easy to implement method that can be flexibly applied to MTL, MDL, ZSL, and ZSDA.