Joint Learning of Multiple Latent Domains and Deep Representations for Domain Adaptation.

Abstract

In domain adaptation, the automatic discovery of multiple latent source domains has succeeded by capturing the intrinsic structure underlying the source data. Different from previous works that mainly rely on shallow models for domain discovery, we propose a novel unified framework based on deep neural networks to jointly address latent domain prediction from source data and deep representation learning from both source and target data. Within this framework, an iterative algorithm is proposed to alternate between 1) utilizing a new probabilistic hierarchical clustering method to separate the source domain into latent clusters and 2) training deep neural networks by using the domain membership as the supervision to learn deep representations. The key idea behind this joint learning framework is that good representations can help to improve the prediction accuracy of latent domains and, in turn, domain prediction results can provide useful supervisory information for feature learning. During the training of the deep model, a domain prediction loss, a domain confusion loss, and a task-specific classification loss are effectively integrated to enable the learned feature to distinguish between different latent source domains, transfer between source and target domains, and become semantically meaningful among different classes. Trained in an end-to-end fashion, our framework outperforms the state-of-the-art methods for latent domain discovery, as validated by extensive experiments on both object classification and human action-recognition tasks.