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Heterogeneous Domain Adaptation With Adversarial Neural Representation Learning: Experiments on E-Commerce and

Mohammadreza Ebrahimi, Yidong Chai, Hao Helen Zhang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 29, 2022
    PubMed
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    This study introduces Heterogeneous Adversarial Neural Domain Adaptation (HANDA), a new framework for adapting machine learning models to new domains with limited data. HANDA significantly improves predictive performance in heterogeneous feature spaces, particularly for e-commerce applications.

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computer Science

    Background:

    • Supervised learning faces challenges adapting models to new domains with scarce data.
    • Domain adaptation methods aim to transfer knowledge from source to target domains with different data distributions.
    • Heterogeneous Domain Adaptation (HDA) is particularly difficult due to differing feature spaces.

    Purpose of the Study:

    • To develop a novel framework for effective domain adaptation in heterogeneous feature spaces.
    • To enhance the transferability of neural representations in cross-domain learning scenarios.
    • To address limitations of existing HDA methods that rely on mathematical optimization and suffer from low transferability.

    Main Methods:

    • Proposed Heterogeneous Adversarial Neural Domain Adaptation (HANDA) framework.

    Related Experiment Videos

  • Employed a unified neural network architecture for feature and distribution alignment.
  • Utilized adversarial kernel learning to achieve domain invariance.

    • Main Results:

    • HANDA demonstrated statistically significant improvements in predictive performance.
    • Evaluated against state-of-the-art HDA methods on major e-commerce image and text benchmarks.
    • Showcased practical utility in real-world dark web online market analysis.

    Conclusions:

    • HANDA effectively maximizes transferability in heterogeneous environments.
    • The framework represents a significant advancement for domain adaptation in e-commerce.
    • HANDA offers a promising solution for leveraging knowledge across domains with differing feature spaces.