Multi-source Distilling Domain Adaptation (1911.11554v2)

Abstract: Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA). Conventional DA methods usually assume that the labeled data is sampled from a single source distribution. However, in practice, labeled data may be collected from multiple sources, while naive application of the single-source DA algorithms may lead to suboptimal solutions. In this paper, we propose a novel multi-source distilling domain adaptation (MDDA) network, which not only considers the different distances among multiple sources and the target, but also investigates the different similarities of the source samples to the target ones. Specifically, the proposed MDDA includes four stages: (1) pre-train the source classifiers separately using the training data from each source; (2) adversarially map the target into the feature space of each source respectively by minimizing the empirical Wasserstein distance between source and target; (3) select the source training samples that are closer to the target to fine-tune the source classifiers; and (4) classify each encoded target feature by corresponding source classifier, and aggregate different predictions using respective domain weight, which corresponds to the discrepancy between each source and target. Extensive experiments are conducted on public DA benchmarks, and the results demonstrate that the proposed MDDA significantly outperforms the state-of-the-art approaches. Our source code is released at: https://github.com/daoyuan98/MDDA.

Multi-source Distilling Domain Adaptation: An Overview

The paper "Multi-source Distilling Domain Adaptation" introduces a novel approach to multidomain adaptation by addressing limitations prevalent in existing methods. This approach, termed the Multi-source Distilling Domain Adaptation (MDDA) network, effectively enhances the transferability of learned models across multiple different domains to an unlabeled target domain, thus outperforming current state-of-the-art techniques.

The primary motivation for this work stems from the inherent domain shift problem experienced when deploying deep neural networks on data from differing distributions, leading to performance degradation. Traditional domain adaptation (DA) strategies are often limited by their single-domain training data assumptions, which fail to leverage the diverse data available from multiple sources that might be present in practical scenarios.

Key Contributions

This research makes several significant contributions to the field of domain adaptation:

1. MDDA Framework: The MDDA framework is structured into four distinct stages, encompassing classifier pre-training, adversarial feature alignment using empirical Wasserstein distance, source sample distillation, and weighted prediction aggregation. This articulated process promotes accurate mapping and classification across domains.
2. Adversarial Training: Unlike symmetrically mapping sources and targets into a single feature space, MDDA asymmetrically aligns target features to source-specific spaces using adversarial training, which helps in stabilizing gradients and addressing distribution non-overlaps, avoiding potential oscillations during optimization.
3. Source Distilling and Fine-tuning: The paper introduces source distilling, which involves selecting and fine-tuning source samples closer in distribution to the target, effectively improving classifier specialization and consequently the accuracy of predictions on target data.
4. Novel Weighting Strategy: MDDA introduces a dynamic weighting mechanism to compute domain-specific influences, emphasizing sources more reflective of the target domain and suppressing those with lesser relevance. This is achieved by utilizing the estimated Wasserstein distances, providing a more nuanced and effective source aggregation method.
5. Evaluation and Results: Extensive experimentation conducted on benchmark datasets such as Digits-five and Office-31 demonstrates the efficacy of MDDA, achieving accuracy improvements of up to 3.3% over contemporary methodologies like the Deep Cocktail Network (DCTN) and others.

Implications and Future Directions

The presented research holds substantial potential for both theoretical advancements and practical applications within AI and domain adaptation. From a theoretical standpoint, the incorporation of empirical Wasserstein distances offers a new perspective on domain heterogeneity and invariance, suggesting further explorations into non-Euclidean distance measures for other adaptation challenges.

Practically, MDDA's robust framework is highly adaptable to various scenarios where source data diversity exists, such as cross-camera video data integration or heterogeneous medical imaging datasets, thereby broadening its utility across domains. Future research could expand upon these principles by integrating generative modeling approaches to further refine domain invariance or investigating scalability in large-scale real-world applications.

In conclusion, the paper successfully addresses key limitations in multi-source domain adaptation, proposing a comprehensive framework with significant empirical support. These contributions mark essential progress in advancing adaptive learning capabilities for systems facing real-world variability.