Deep Hashing Network for Unsupervised Domain Adaptation (1706.07522v1)

Published 22 Jun 2017 in cs.CV

Abstract: In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a large amount of labeled data, which is an expensive process in terms of time, labor and human expertise. Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain. Further, the explosive growth of digital data has posed a fundamental challenge concerning its storage and retrieval. Due to its storage and retrieval efficiency, recent years have witnessed a wide application of hashing in a variety of computer vision applications. In this paper, we first introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms. The dataset contains images of a variety of everyday objects from multiple domains. We then propose a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes, to accurately classify unseen target data. To the best of our knowledge, this is the first research effort to exploit the feature learning capabilities of deep neural networks to learn representative hash codes to address the domain adaptation problem. Our extensive empirical studies on multiple transfer tasks corroborate the usefulness of the framework in learning efficient hash codes which outperform existing competitive baselines for unsupervised domain adaptation.

Authors (4)

Hemanth Venkateswara (17 papers)
Jose Eusebio (1 paper)
Shayok Chakraborty (6 papers)
Sethuraman Panchanathan (10 papers)

Citations (1,889)

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Summary

Deep Hashing Network for Unsupervised Domain Adaptation

The paper "Deep Hashing Network for Unsupervised Domain Adaptation" by Hemanth Venkateswara et al. addresses the challenge of unsupervised domain adaptation using deep neural networks to generate efficient hash codes. This work introduces a framework integrating deep learning with hashing techniques to leverage labeled data from a source domain and learn from unlabeled target data. This integration caters to two significant practical concerns: reducing the burden of data labeling and mitigating the storage and retrieval challenges inherent in the age of big data.

Core Contributions

1. Proposed Method: Domain Adaptive Hashing (DAH)

The authors propose the DAH network, which exploits the feature learning capabilities of deep neural networks to learn representative hash codes explicitly designed for domain adaptation. The backbone of the DAH network is a deep Convolutional Neural Network (CNN) inspired by the VGG-F architecture, pre-trained on the ImageNet dataset, and subsequently fine-tuned for domain adaptation tasks.

The DAH network includes:

Supervised Hash Loss: To ensure that hash values for same-class samples are similar.
Unsupervised Entropy Loss: To align the unlabeled target data with the source categories based on feature similarity.
Multi-Kernel Maximum Mean Discrepancy (MK-MMD): To minimize domain disparity by aligning feature distributions across domains at multiple network layers.

2. Novel Dataset: Office-Home

The authors introduce the Office-Home dataset, comprising approximately 15,500 images across 65 categories collected from four distinct domains (Art, Clipart, Product, and Real-World). This dataset, designed to validate domain adaptation algorithms, addresses the insufficiency of existing datasets like Office or Office-Caltech in training and evaluating deep learning models.

Empirical Evaluation

The authors conducted extensive experiments on standard benchmarks such as the Office and Office-Home datasets to evaluate the DAH's performance.

Unsupervised Domain Adaptation Results:

DAH's performance was compared against several baselines, including GFK, TCA, CORAL, JDA, and advanced deep learning approaches like DAN and DANN. DAH exhibited superior performance, especially within the context of the larger Office-Home dataset, indicating its robust capability in domain adaptation across a significant number of categories.

For instance, DAH attained an average accuracy of 45.54% on the Office-Home dataset, outperforming DAN and DANN, whose accuracies were 43.46% and 44.94%, respectively.

Unsupervised Domain Adaptive Hashing Results:

The authors also evaluated the learned hash codes' efficacy for classifying unseen test instances without any target domain labels. Comparisons included unsupervised hashing methods like ITQ, KMeans, and state-of-the-art techniques such as BA and BDNN. DAH demonstrated notable improvements, reflected in precision-recall curves and Mean Average Precision (mAP) measurements, particularly when hash lengths of 64 bits were utilized. DAH's average mAP stood at 0.480, significantly outperforming the baseline methods.

Detailed Analysis

The authors provided a thorough analysis of feature representations using t-SNE embeddings and $\mathcal{A}$ -distance measures to visually and quantitatively demonstrate domain alignment efficacy. Such analyses underpinned DAH’s strength in minimizing domain disparity and enhancing feature clustering, even under the challenging unsupervised settings.

Implications and Future Directions

Practical Implications:

The proposed DAH framework effectively addresses the dual challenges of hashing and domain adaptation. This integration can significantly impact real-world applications where labeled data is scarce but related domain data is available. Efficiently retrieving and categorizing large datasets with minimal storage requirements becomes feasible.

Theoretical Implications:

From a theoretical standpoint, the combination of supervised component construction for hashing with unsupervised entropy-based alignment marks a significant stride in unsupervised domain adaptation. The MK-MMD approach further ensures robust cross-domain feature representation, facilitating better generalization.

Future Developments:

Possible future directions include exploring adversarial training methods within the DAH framework to further enhance domain alignment. Another interesting direction would be to investigate the implications of varying hash code lengths and their distributions to optimize both storage and retrieval efficiencies.

Lastly, extending the Office-Home dataset for more diverse domains and categories could provide a richer evaluation ground for future domain adaptation algorithms.

This paper is an essential step towards more adaptive and efficient machine learning models that can seamlessly transfer knowledge across different domains, paving the way for practical, real-world intelligent systems.

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