M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning (1807.02552v1)

Abstract: Unsupervised domain adaptation techniques have been successful for a wide range of problems where supervised labels are limited. The task is to classify an unlabeled target' dataset by leveraging a labeledsource' dataset that comes from a slightly similar distribution. We propose metric-based adversarial discriminative domain adaptation (M-ADDA) which performs two main steps. First, it uses a metric learning approach to train the source model on the source dataset by optimizing the triplet loss function. This results in clusters where embeddings of the same label are close to each other and those with different labels are far from one another. Next, it uses the adversarial approach (as that used in ADDA \cite{2017arXiv170205464T}) to make the extracted features from the source and target datasets indistinguishable. Simultaneously, we optimize a novel loss function that encourages the target dataset's embeddings to form clusters. While ADDA and M-ADDA use similar architectures, we show that M-ADDA performs significantly better on the digits adaptation datasets of MNIST and USPS. This suggests that using metric-learning for domain adaptation can lead to large improvements in classification accuracy for the domain adaptation task. The code is available at \url{https://github.com/IssamLaradji/M-ADDA}.

• The paper introduces a novel unsupervised domain adaptation method that combines triplet loss-based metric learning with adversarial strategies.
• It leverages a center magnet loss to enforce cluster formation in the target domain, aligning feature distributions with the labeled source.
• Empirical evaluations on standard digits datasets demonstrate significant performance improvements, with accuracies reaching up to 95.2%.

Overview of M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning

The paper presents M-ADDA, a novel approach to tackle the unsupervised domain adaptation problem by combining metric-based learning with adversarial strategies. This method aims to classify an unlabeled target dataset by exploiting a similar but labeled source dataset, addressing the challenge posed by the domain shift phenomenon that often compromises the transferability of machine learning models across different datasets.

The proposed approach is structured around two core components. Initially, the source model is trained to optimize the triplet loss function, specifically designed for metric learning, which organizes the source dataset into well-defined clusters. This triplet loss encourages examples of the same class to be close together in the embedding space while maximizing the distance between examples from different classes. In the subsequent phase, an adversarial learning method, inspired by the Adversarial Discriminative Domain Adaptation (ADDA), is applied to make the feature distributions extracted from both source and target datasets indistinguishable. Simultaneously, a novel center magnet loss function is introduced to further enforce the formation of clusters in the target domain embeddings, akin to those achieved in the source domain.

The experimental evaluation, conducted on standard digits datasets such as MNIST and USPS, demonstrates substantial improvements over existing methods, including a significant performance boost compared to ADDA. The paper concludes that metric learning approaches, particularly those leveraging deep triplet networks, offer a promising avenue for enhancing the effectiveness of unsupervised domain adaptation.

Key Contributions and Results

1. Metric-Based Learning Approach: The foundational contribution lies in integrating metric learning into the domain adaptation framework. This strategy leverages the triplet loss to pre-train the source model, culminating in well-separated clusters in the feature space.
2. Adversarial and Cluster-Based Regularization: By combining adversarial learning for domain adaptation and the center magnet loss for cluster formation, M-ADDA achieves a notable alignment between source and target feature distributions, ensuring structural coherency in the target domain embeddings.
3. Empirical Validation: The proposed method delivers superior performance, achieving a classification accuracy of 95.2% on MNIST to USPS adaptation and 94.0% on the reverse, markedly surpassing the results of ADDA.

Implications and Future Directions

The implications of M-ADDA are substantial for both theoretical and practical aspects of domain adaptation research. The integration of metric learning emphasizes the potential of non-parametric and distribution-agnostic methods in accommodating domain shifts. Furthermore, the empirical success on digit datasets suggests that M-ADDA could potentially be scaled to more complex and high-dimensional domains, although this remains to be validated in future work.

Moving forward, future research might explore the adaptability of M-ADDA to other forms of data, such as text or time-series, where domain shifts are prevalent. Additionally, further validation on datasets with larger domain discrepancies, such as those entailed by the VisDA challenge, would shed light on the robustness of this approach. Investigating the interplay between the triplet loss's cluster-forming capability and the adversarial adaptation's distribution alignment could yield deeper insights into optimizing these methods for broader application contexts in AI.