Label Efficient Learning of Transferable Representations across Domains and Tasks (1712.00123v1)

Abstract: We propose a framework that learns a representation transferable across different domains and tasks in a label efficient manner. Our approach battles domain shift with a domain adversarial loss, and generalizes the embedding to novel task using a metric learning-based approach. Our model is simultaneously optimized on labeled source data and unlabeled or sparsely labeled data in the target domain. Our method shows compelling results on novel classes within a new domain even when only a few labeled examples per class are available, outperforming the prevalent fine-tuning approach. In addition, we demonstrate the effectiveness of our framework on the transfer learning task from image object recognition to video action recognition.

Label Efficient Learning of Transferable Representations across Domains and Tasks

The paper "Label Efficient Learning of Transferable Representations across Domains and Tasks" presents a framework aimed at enhancing the efficiency of learning transferable representations in machine learning models, particularly neural networks. It focuses on reducing the dependency on labeled data, achieving effective domain adaptation, and generalizing across domains and tasks. The framework introduces a novel approach that combines domain adversarial loss with metric learning-based strategies to learn representations that generalize well even with sparse labeled data in the target domain.

The authors address the challenge of domain shift, which occurs when the learning model is trained and tested on data drawn from different distributions. The proposed framework uses a domain adversarial loss to mitigate this issue by aligning the source and target domain data distributions. Furthermore, the incorporation of a metric learning-based approach facilitates generalization to novel tasks by establishing cross-domain and within-domain class similarities. This dual-strategy framework is optimized using both well-labeled source domain data and minimally labeled or completely unlabeled target domain data, setting it apart from traditional supervised and fine-tuning approaches.

Empirical results demonstrate that the proposed method outperforms prevalent fine-tuning techniques, especially in scenarios with novel classes within a new domain and limited labeled examples per class. Notably, experiments conducted from transferring knowledge from image object recognition tasks to video action recognition tasks illustrate the method's strong performance.

Key Contributions and Methodological Insights

1. Domain Adversarial Learning: The framework introduces a multi-layer domain adversarial formulation leveraging adversarial discriminative models to align representations across domains. This approach ensures that the model learns domain-invariant features, thereby improving domain adaptation efficacy.
2. Metric Learning-based Transfer: To facilitate task generalization, the approach uses a metric learning-based component, which is vital for analyzing cross-domain class similarities. This component significantly contributes to the model's ability to handle few-shot learning scenarios effectively.
3. Joint Optimization Framework: The model's ability to jointly optimize labeled source data and unlabeled/sparsely labeled target data is central to achieving label-efficient learning. By simultaneously training the model with these two data sources, the framework provides robust support for both existing and novel tasks in the target domain.
4. Numerical Evaluation and Results: Empirical evaluation highlights the effectiveness of the proposed method across different tasks and domains. In particular, the results on digit domain adaptation (SVHN to MNIST) and image-to-video transfer learning demonstrate substantial gains over existing models, showcasing improved accuracy with limited labeled target domain data.

Implications and Future Directions

The implications of this research are significant for tasks where labeled data is scarce or expensive to acquire. The ability to efficiently transfer knowledge across different but related domains using a minimal amount of labeled examples reduces the overall data labeling cost and makes the deployment of machine learning models more viable in practical situations. This approach is particularly beneficial in fields like computer vision and natural language processing, where domain shifts and task generalization are prevalent issues.

In future developments, the research could explore extending the framework to more complex multi-domain scenarios where multiple source domains are present or where the target domain is continuously evolving. Additionally, integrating more sophisticated domain-based adversarial strategies or exploring alternative unsupervised learning methods could potentially augment the adaptability of the transfer learning process.

Conclusion

This paper presents a comprehensive and well-founded approach to address label-efficient learning and transfer of domain and task representations. By leveraging a combination of adversarial and metric learning strategies, it offers a viable solution to tackle domain shift and learning efficiency challenges in modern machine learning applications. Through rigorous experimentation and novel methodological integrations, it establishes a robust platform for future exploration and enhancement in transfer learning domains.