2 research outputs found
Semi-supervised representation learning via dual autoencoders for domain adaptation
Domain adaptation aims to exploit the knowledge in source domain to promote
the learning tasks in target domain, which plays a critical role in real-world
applications. Recently, lots of deep learning approaches based on autoencoders
have achieved a significance performance in domain adaptation. However, most
existing methods focus on minimizing the distribution divergence by putting the
source and target data together to learn global feature representations, while
they do not consider the local relationship between instances in the same
category from different domains. To address this problem, we propose a novel
Semi-Supervised Representation Learning framework via Dual Autoencoders for
domain adaptation, named SSRLDA. More specifically, we extract richer feature
representations by learning the global and local feature representations
simultaneously using two novel autoencoders, which are referred to as
marginalized denoising autoencoder with adaptation distribution (MDAad) and
multi-class marginalized denoising autoencoder (MMDA) respectively. Meanwhile,
we make full use of label information to optimize feature representations.
Experimental results show that our proposed approach outperforms several
state-of-the-art baseline methods.Comment: This paper has been accepted by the journal of KNOWLEDGE-BASED
SYSTEMS (KBS) 201
Learning causal representations for robust domain adaptation
Domain adaptation solves the learning problem in a target domain by
leveraging the knowledge in a relevant source domain. While remarkable advances
have been made, almost all existing domain adaptation methods heavily require
large amounts of unlabeled target domain data for learning domain invariant
representations to achieve good generalizability on the target domain. In fact,
in many real-world applications, target domain data may not always be
available. In this paper, we study the cases where at the training phase the
target domain data is unavailable and only well-labeled source domain data is
available, called robust domain adaptation. To tackle this problem, under the
assumption that causal relationships between features and the class variable
are robust across domains, we propose a novel Causal AutoEncoder (CAE), which
integrates deep autoencoder and causal structure learning into a unified model
to learn causal representations only using data from a single source domain.
Specifically, a deep autoencoder model is adopted to learn low-dimensional
representations, and a causal structure learning model is designed to separate
the low-dimensional representations into two groups: causal representations and
task-irrelevant representations. Using three real-world datasets the extensive
experiments have validated the effectiveness of CAE compared to eleven
state-of-the-art methods