Beyond Sharing Weights for Deep Domain Adaptation (1603.06432v2)

Abstract: The performance of a classifier trained on data coming from a specific domain typically degrades when applied to a related but different one. While annotating many samples from the new domain would address this issue, it is often too expensive or impractical. Domain Adaptation has therefore emerged as a solution to this problem; It leverages annotated data from a source domain, in which it is abundant, to train a classifier to operate in a target domain, in which it is either sparse or even lacking altogether. In this context, the recent trend consists of learning deep architectures whose weights are shared for both domains, which essentially amounts to learning domain invariant features. Here, we show that it is more effective to explicitly model the shift from one domain to the other. To this end, we introduce a two-stream architecture, where one operates in the source domain and the other in the target domain. In contrast to other approaches, the weights in corresponding layers are related but not shared. We demonstrate that this both yields higher accuracy than state-of-the-art methods on several object recognition and detection tasks and consistently outperforms networks with shared weights in both supervised and unsupervised settings.

• The paper presents a two-stream CNN that replaces strict weight sharing with learnable linear transformations to adapt features more flexibly.
• It employs a weighted Maximum Mean Discrepancy loss to align source and target distributions, improving classification accuracy by about 10% in experiments.
• The approach enables robust adaptation between synthetic and real domains, offering scalable applications in aerial imaging and facial landmark estimation.

Insights into "Beyond Sharing Weights for Deep Domain Adaptation"

The paper entitled "Beyond Sharing Weights for Deep Domain Adaptation," authored by Artem Rozantsev, Mathieu Salzmann, and Pascal Fua, presents an innovative approach to the domain adaptation problem in deep learning. Domain adaptation is a technique aimed at improving the performance of a classifier trained on one domain when it is deployed to an unseen but related target domain. Traditional methods have relied heavily on sharing a deep learning architecture with identical weights across both domains, primarily focusing on learning domain-invariant features. This paper challenges this foundational principle by proposing an alternative framework.

Core Contribution

The authors introduce a two-stream convolutional neural network (CNN) architecture in which the weight parameters in the corresponding layers for the source and target domains are related but not identical. This architectural design deviates from the standard practice of weight sharing to better accommodate the domain shift's underlying characteristics. The proposed method utilizes a novel loss function that allows these weights to be linear transformations of one another, providing the flexibility to enhance the target domain's feature discriminability without strictly enforcing invariance. Additionally, the methodology incorporates weighted Maximum Mean Discrepancy (MMD) into the regularization process to modulate the distributional alignment between source and target representations.

Numerical Results and Comparisons

Through experimental evaluations across various datasets such as UAV imagery and the established Office dataset, the proposed method consistently outperforms state-of-the-art models, including those based on shared weights. For instance, in the UAV dataset involving synthetic-to-real domain adaptation, this methodology outstrips existing shared-weight frameworks by approximately 10% in terms of average precision. This pattern of robust improvement is similarly reflected in the Office dataset experiments, wherein the approach surpasses prominent domain adaptation models like DDC and GRL in unsupervised scenarios. These empirical insights underscore the utility of selectively unsharing weights to account for domain-specific peculiarities, demonstrating enhanced performance beyond conventional weight-sharing schemes.

Theoretical and Practical Implications

Theoretically, the paper posits that striving for domain-invariant features may be suboptimal if it comes at the cost of suppressing features that are critical to discriminative tasks. The introduction of a flexible transformation function between domains allows the model to learn both common and domain-specific components, which may reflect more realistic distributions spanning domain pairs.

Practically, the two-stream architecture has broad implications: it promotes leveraging synthetic data as a viable and scalable augmentation strategy for domains with limited labeled data—a reality encountered in many real-world applications such as aerial drone detection and facial landmark estimation. Moreover, by presenting an adaptable loss formulation, it can potentially mitigate overfitting in target domains with scarce labels.

Future Directions

Potential areas for future research include exploring more complex non-linear transformation functions between the source and target domain weights, which might yield further performance gains. Investigating domain adaptation in frameworks beyond CNNs, such as transformer models, could also extend the applicability of these insights. Additionally, automated methods for identifying the optimal set of non-shared layers could improve the model’s adaptability to new domain pairs without significant manual intervention.

In summary, this work represents a pivotal advance in domain adaptation, breaking from the constraints of traditional domain-invariant feature learning and setting the stage for more adaptive and flexible solutions in cross-domain challenges.