Domain-Specific Batch Normalization for Unsupervised Domain Adaptation (1906.03950v1)

Abstract: We propose a novel unsupervised domain adaptation framework based on domain-specific batch normalization in deep neural networks. We aim to adapt to both domains by specializing batch normalization layers in convolutional neural networks while allowing them to share all other model parameters, which is realized by a two-stage algorithm. In the first stage, we estimate pseudo-labels for the examples in the target domain using an external unsupervised domain adaptation algorithm---for example, MSTN or CPUA---integrating the proposed domain-specific batch normalization. The second stage learns the final models using a multi-task classification loss for the source and target domains. Note that the two domains have separate batch normalization layers in both stages. Our framework can be easily incorporated into the domain adaptation techniques based on deep neural networks with batch normalization layers. We also present that our approach can be extended to the problem with multiple source domains. The proposed algorithm is evaluated on multiple benchmark datasets and achieves the state-of-the-art accuracy in the standard setting and the multi-source domain adaption scenario.

• The paper presents a DSBN framework that leverages separate normalization parameters to distinguish domain-specific features from invariant ones.
• It employs a two-stage training process, starting with pseudo-label generation and refining predictions through iterative self-training.
• Empirical evaluations show significant accuracy gains over traditional BN, enhancing multi-source domain adaptability and robustness to domain shifts.

Domain-Specific Batch Normalization for Unsupervised Domain Adaptation: A Comprehensive Review

The paper outlines a novel framework leveraging Domain-Specific Batch Normalization (DSBN) to enhance unsupervised domain adaptation. It capitalizes on the inherent capabilities of deep neural networks, notably convolutional neural networks (CNNs), to navigate the challenges posed by domain shifts across datasets. The aim is to develop a robust mechanism to transfer learning from annotated source domains to unlabeled target domains, crucial for extending machine learning models' applicability beyond constrained and homogeneous data environments.

Methodology and Key Concepts

At the core of the framework is the integration of DSBN into CNN architectures for domain adaptation tasks. Traditional batch normalization (BN) layers standardize each batch of inputs independently but do not consider domain-specific characteristics. DSBN, however, employs separate normalization parameters for each domain, maintaining shared model parameters across domains otherwise. This separation fosters a refined extraction of domain-specific features while preserving domain-invariant representations.

The framework unfolds over two distinct stages:

1. Initial Pseudo-Label Generation: Utilizing existing domain adaptation methods such as MSTN or CPUA, initial pseudo-labels in the target domain are established. By introducing DSBN, the networks are trained with a structural emphasis on domain-specific normalization, leading to a more precise alignment with target domain characteristics.
2. Self-training with Pseudo Labels: Building upon the initial pseudo labels, a refined multi-task classification model is trained. The process iteratively updates pseudo labels, progressively enhancing classification performance by dynamically recalibrating the weightage between initial pseudo-labels and the model's evolving predictions.

Empirical Evaluation and Results

The proposed model exhibits state-of-the-art performance across several benchmark datasets like Office-31 and VisDA-C, validating its effectiveness against leading existing approaches. Notably, classification accuracy is consistently improved when both training stages incorporating DSBN are employed. The numerical results convincingly demonstrate that DSBN-layered models enhance accuracy substantially over standard BN models, especially under challenging domain scenarios.

Moreover, the extension to multi-source domain adaptation is seamlessly achieved by expanding DSBN to accommodate multiple domain branches, showcasing enhanced adaptability and performance over singular or merged source domain models. The demonstration of incremental gains through iterative re-training highlights the framework’s robustness in continuously refining domain adaptation capabilities.

Implications and Future Directions

The introduction of DSBN paves the way for a more nuanced understanding of domain-specific variables in domain adaptation tasks. It contributes a methodical approach to disentangle domain-specific knowledge from the invariant features crucial for unsupervised learning paradigms. This technique promises extensive applicability not only in computer vision tasks but potentially across varied domains where domain shifts present significant hurdles.

Future exploration can explore optimizing the integration of DSBN with other neural network components or expanding the approach into a fully semi-supervised learning framework. Additionally, collaborative endeavors could investigate bridging DSBN’s capabilities with emerging fields such as few-shot learning and generative models, thereby broadening its spectrum of application.

In conclusion, through DSBN, this paper sets a precedent for more fine-tuned, adaptable, and accurate domain adaptation models, marking a significant stride in overcoming the domain shift dilemma inherent in unsupervised learning tasks.