Dual-Representation-Based Autoencoder for Domain Adaptation

Preserving domain private information via mutual information maximization

Recent advances in unsupervised domain adaptation have shown that mitigating the domain divergence by extracting the domain-invariant features could significantly improve the generalization of a model with respect to a new data domain. However, current methodologies often neglect to retain domain private information, which is the unique information inherent to the unlabeled new domain, compromising generalization. This paper presents a novel method that utilizes mutual information to protect this domain-specific information, ensuring that the latent features of the unlabeled data not only remain domain-invariant but also reflect the unique statistics of the unlabeled domain. We show that simultaneous maximization of mutual information and reduction of domain divergence can effectively preserve domain-private information. We further illustrate that a neural estimator can aptly estimate the mutual information between the unlabeled input space and its latent feature space. Both theoretical analysis and empirical results validate the significance of preserving such unique information of the unlabeled domain for cross-domain generalization. Comparative evaluations reveal our method's superiority over existing state-of-the-art techniques across multiple benchmark datasets.

Semi-Supervised Domain Alignment Learning for Single Image Dehazing

Convolutional neural networks (CNNs) have attracted much research attention and achieved great improvements in single-image dehazing. However, previous learning-based dehazing methods are mainly trained on synthetic data, which greatly degrades their generalization capability on natural hazy images. To address this issue, this article proposes a semi-supervised learning approach for single-image dehazing, where both synthetic and realistic images are leveraged during training. Considering the situation that it is hard to obtain the realistic pairs of hazy and haze-free images, how to utilize the realistic data is not a trivial work. In this article, a domain alignment module is introduced to narrow the distribution distance between synthetic data and realistic hazy images in a latent feature space. Meanwhile, a haze-aware attention module is designed to describe haze densities of different regions in the image, thus adaptively responds for different hazy areas. Furthermore, the dark channel prior is introduced to the framework to improve the quality of the unsupervised learning results by considering the statistical characters of haze-free images. Such a semi-supervised design can significantly address the domain shift issue between the synthetic and realistic data, and improve generalization performance in the real world. Experiments indicate that the proposed method obtains state-of-the-art performance on both public synthetic and realistic hazy images with better visual results.

Guided Discrimination and Correlation Subspace Learning for Domain Adaptation

As a branch of transfer learning, domain adaptation leverages useful knowledge from a source domain to a target domain for solving target tasks. Most of the existing domain adaptation methods focus on how to diminish the conditional distribution shift and learn invariant features between different domains. However, two important factors are overlooked by most existing methods: 1) the transferred features should be not only domain invariant but also discriminative and correlated, and 2) negative transfer should be avoided as much as possible for the target tasks. To fully consider these factors in domain adaptation, we propose a guided discrimination and correlation subspace learning (GDCSL) method for cross-domain image classification. GDCSL considers the domain-invariant, category-discriminative, and correlation learning of data. Specifically, GDCSL introduces the discriminative information associated with the source and target data by minimizing the intraclass scatter and maximizing the interclass distance. By designing a new correlation term, GDCSL extracts the most correlated features from the source and target domains for image classification. The global structure of the data can be preserved in GDCSL because the target samples are represented by the source samples. To avoid negative transfer issues, we use a sample reweighting method to detect target samples with different confidence levels. A semi-supervised extension of GDCSL (Semi-GDCSL) is also proposed, and a novel label selection scheme is introduced to ensure the correction of the target pseudo-labels. Comprehensive and extensive experiments are conducted on several cross-domain data benchmarks. The experimental results verify the effectiveness of the proposed methods over state-of-the-art domain adaptation methods.