Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach (1810.11547v1)

Abstract: Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where there is a single, labeled, source and a single target domain. However, in many real-world settings one seeks to adapt to multiple, but somewhat similar, target domains. Applying pairwise adaptation approaches to this setting may be suboptimal, as they fail to leverage shared information among multiple domains. In this work we propose an information theoretic approach for domain adaptation in the novel context of multiple target domains with unlabeled instances and one source domain with labeled instances. Our model aims to find a shared latent space common to all domains, while simultaneously accounting for the remaining private, domain-specific factors. Disentanglement of shared and private information is accomplished using a unified information-theoretic approach, which also serves to establish a stronger link between the latent representations and the observed data. The resulting model, accompanied by an efficient optimization algorithm, allows simultaneous adaptation from a single source to multiple target domains. We test our approach on three challenging publicly-available datasets, showing that it outperforms several popular domain adaptation methods.

Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach

The paper "Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach" introduces a novel methodology for tackling the challenge of unsupervised domain adaptation (uDA) in scenarios involving multiple unlabeled target domains. This is a deviation from traditional uDA models which largely focus on pairwise adaptation between a single source and a single target domain. The authors address the inadequacies of pairwise approaches in leveraging shared information among multiple domains and propose an information-theoretic framework to enhance the adaptability and performance of models across multiple domains.

Key Methodological Insights:

1. Shared and Private Latent Spaces: The proposed approach involves constructing a shared latent space that is common across all domains while simultaneously identifying private, domain-specific factors. This separation enables efficient disentanglement of shared and private information, which is pivotal for effective domain adaptation.
2. Optimization Strategy: The model employs a unified information-theoretic approach that maximizes mutual information between feature representations and observed data, thus strengthening the relationship between latent representations and the actual data. Efficient optimization is achieved through an approach akin to adversarial training, striking a balance between preserving sample reconstruction fidelity and ensuring domain and class label discrimination.
3. Multi-Target Domain Adaptation: The paper extends the paradigm of domain adaptation from a single-source-single-target domain to a single-source-multi-target domain. This allows simultaneous adaptation from a labeled source to multiple unlabeled target domains, improving model generalization across diverse datasets.

Experimental Validation:

The authors have tested their approach on three benchmark datasets: digit classification (MNIST, MNIST-M, SVHN, USPS), Multi-PIE expression recognition, and PACS multi-domain image recognition. The results demonstrate superior performance compared to several popular domain adaptation methods, including CORAL, DANN, and DSN, indicating the efficacy of shared latent space exploitation.

1. Digits Dataset: The method achieved enhanced classification accuracy, outperforming other approaches in most source-target transformations. The joint adaptation of related domains was shown to be superior to pairwise adaptations and naïve combinations of multiple domains.
2. Multi-PIE Dataset: Adaptation was tested over different camera angles, showcasing substantial improvements even under challenging conditions where image structures vary significantly.
3. PACS Dataset: This task indicated superiority in handling extreme depiction style differences that augment the complexity of adaptation scenarios.

Implications and Future Directions:

The implications of this research are profound in practical applications involving real-time data from multiple sources, such as image recognition systems in varied environmental conditions and sensor modalities. The theoretical foundation laid by the information-theoretic approach sets a precedent for further exploration into optimizing unsupervised domain adaptation using probabilistic modeling techniques.

Looking forward, the methodology could be extended to incorporate additional layers of data complexity, such as temporal dependencies or hierarchical domain structures, opening avenues for research in dynamic environments and multi-modal data contexts. Furthermore, speculative developments may include integration with reinforcement learning systems where adaptable domain representations can significantly benefit decision-making processes.

Given the ongoing advancements in artificial intelligence, the approach outlined in this paper provides a compelling direction for future research in enhancing domain adaptation capabilities, ensuring robustness and reliability across various real-world applications.