Title: Zero-shot Domain Adaptation without Domain Semantic Descriptors,
Abstract: We propose a method to infer domain-specific models such as classifiers for
unseen domains, from which no data are given in the training phase, without
domain semantic descriptors. When training and test distributions are
different, standard supervised learning methods perform poorly. Zero-shot
domain adaptation attempts to alleviate this problem by inferring models that
generalize well to unseen domains by using training data in multiple source
domains. Existing methods use observed semantic descriptors characterizing
domains such as time information to infer the domain-specific models for the
unseen domains. However, it cannot always be assumed that such metadata can be
used in real-world applications. The proposed method can infer appropriate
domain-specific models without any semantic descriptors by introducing the
concept of latent domain vectors, which are latent representations for the
domains and are used for inferring the models. The latent domain vector for the
unseen domain is inferred from the set of the feature vectors in the
corresponding domain, which is given in the testing phase. The domain-specific
models consist of two components: the first is for extracting a representation
of a feature vector to be predicted, and the second is for inferring model
parameters given the latent domain vector. The posterior distributions of the
latent domain vectors and the domain-specific models are parametrized by neural
networks, and are optimized by maximizing the variational lower bound using
stochastic gradient descent. The effectiveness of the proposed method was
demonstrated through experiments using one regression and two classification
tasks.