Learning Transferable Features with Deep Adaptation Networks (1502.02791v2)

Abstract: Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation Network (DAN) architecture, which generalizes deep convolutional neural network to the domain adaptation scenario. In DAN, hidden representations of all task-specific layers are embedded in a reproducing kernel Hilbert space where the mean embeddings of different domain distributions can be explicitly matched. The domain discrepancy is further reduced using an optimal multi-kernel selection method for mean embedding matching. DAN can learn transferable features with statistical guarantees, and can scale linearly by unbiased estimate of kernel embedding. Extensive empirical evidence shows that the proposed architecture yields state-of-the-art image classification error rates on standard domain adaptation benchmarks.

• The paper introduces Deep Adaptation Networks, a novel method that aligns source and target domains using multi-kernel MMD, leading to superior transfer performance.
• It leverages pre-trained CNNs and fine-tunes task-specific layers while preserving general features, effectively reducing domain discrepancy.
• Empirical evaluations on benchmarks like Office-31 demonstrate significant performance gains over traditional shallow transfer methods.

Learning Transferable Features with Deep Adaptation Networks

The paper "Learning Transferable Features with Deep Adaptation Networks" by Mingsheng Long et al. proposes a novel architecture designed to maximize the transferability of features in the domain adaptation scenario. The methodology capitalizes on the strengths of deep convolutional neural networks (CNNs) and implements a strategic adaptation mechanism to minimize domain discrepancies, thereby improving the performance of models on target domains where labeled data is limited or non-existent.

Key Innovations

The principal innovation in this work is the introduction of the Deep Adaptation Network (DAN), which extends deep CNNs for domain adaptation. The authors assert that while deep features can generalize well to novel tasks along lower layers of the network, the transferability degrades significantly in the higher layers, which are more task-specific. To address this issue, DAN embeddings are optimized in a reproducing kernel Hilbert space (RKHS) where mean embeddings of source and target domain distributions can be aligned explicitly. This enhancements is achieved through an optimal multi-kernel selection method, improving the statistical matching effectiveness of the domains.

Methodological Overview

1. Deep Adaptation Network (DAN): The network starts with an AlexNet model pre-trained on the ImageNet dataset. It adapts this model for domain-specific layers while preserving the general features of the initial layers. The key layers $fc6$--$fc8$ are specifically targeted to adapt to the new domain while maintaining general features in layers $conv1$--$conv3$ and $conv4$--$conv5$ through fine-tuning.
2. Multi-Kernel Maximum Mean Discrepancy (MK-MMD): The domain discrepancy is minimized by embedding task-specific layers in an RKHS. The MK-MMD criterion is employed to match the mean embeddings of different domain distributions. This method leverages multiple kernels, improving the alignment between the source and target domains beyond the capabilities of single-kernel methods.
3. Scalable Training: A linear-time unbiased estimate of the kernel mean embedding is utilized, ensuring that the approach remains scalable to large datasets. This is crucial for deploying deep learning algorithms effectively.

Empirical Evaluation

Comprehensive empirical evaluations demonstrate the efficacy of the proposed architecture:

• Standard Domain Adaptation Benchmarks: DAN yields state-of-the-art performance on various standard benchmarks, including Office-31 and Office-10 + Caltech-10 datasets.
• Comparison with Existing Methods: DAN consistently outperforms conventional shallow transfer learning methods (e.g., TCA, GFK) and current state-of-the-art deep adaptation networks like DDC. The results confirm the superiority of multi-layer and multi-kernel adaptation strategies employed in DAN.
• Feature Transferability: t-SNE visualizations reveal that features learned by DAN are more discriminative and better aligned between the source and target categories compared to those learned by DDC.

Theoretical Implications and Future Directions

The theoretical grounding, leveraging RKHS for domain embedding, supports a more robust understanding of domain adaptation. The authors suggest that future research could explore adaptive feature learning within convolutional layers themselves, potentially further improving the robustness of deep domain adaptation models. Moreover, identifying the boundary of general and specific features within the network layers could guide more informed adaptation strategies.

Practical Relevance

From a practical standpoint, the advancements presented in this paper could greatly benefit applications requiring model adaptation to new domains with minimal labeled data. Industries relying on image recognition applications, such as autonomous driving or security, could particularly leverage these findings for more efficient model training and adaptation.

In summary, the Deep Adaptation Network (DAN) proposed in this paper presents a compelling step forward in domain adaptation. It integrates the robust feature learning capabilities of deep networks with domain adaptation principles, offering new insights and methodologies that enhance performance on cross-domain tasks. The empirical results and theoretical discussions underscore the potential of DAN to significantly impact the field of machine learning, particularly in applications where domain shift is a critical challenge.