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Related Experiment Video

Updated: Jan 4, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

950

Improved Techniques for Adversarial Discriminative Domain Adaptation.

Aaron Chadha, Yiannis Andreopoulos

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 13, 2019
    PubMed
    Summary

    This study enhances adversarial discriminative domain adaptation (ADDA) for image classification by introducing new loss functions and a novel framework. The improved ADDA framework achieves state-of-the-art performance in unsupervised domain adaptation tasks.

    Related Experiment Videos

    Last Updated: Jan 4, 2026

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    950

    Area of Science:

    • Computer Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Unsupervised domain adaptation (UDA) is crucial for leveraging labeled source data with unlabeled target data in machine learning.
    • Adversarial Discriminative Domain Adaptation (ADDA) is an efficient UDA framework for image classification, but its performance can be further improved.
    • Existing adversarial methods often lack efficiency or competitive accuracy in UDA tasks.

    Purpose of the Study:

    • To enhance the performance of ADDA by proposing a new framework and novel loss formulations for unsupervised domain adaptation.
    • To model the joint distribution over domain and task by extending the discriminator output over source classes.
    • To align target encoder distributions with the source domain using Maximum Mean Discrepancy (MMD) and reconstruction-based losses.

    Main Methods:

    • Extended ADDA framework using semi-supervised Generative Adversarial Networks (GANs) to model joint domain and task distributions.
    • Proposed Maximum Mean Discrepancy (MMD) and reconstruction-based loss functions for aligning target encoder distributions.
    • Introduced regularization techniques, including denoising autoencoder for the discriminator and source example regularization for the target encoder, to stabilize training and reduce overfitting.

    Main Results:

    • The proposed framework and loss formulations demonstrated competitive or superior performance compared to state-of-the-art UDA methods like DIFA and MCDDA across various datasets (MNIST, USPS, SVHN, MNIST-M, Office-31).
    • Achieved lower complexity than other recent adversarial methods while maintaining high performance.
    • Successfully applied and validated the framework on a challenging neuromorphic vision sensing (NVS) sign language recognition dataset, demonstrating its effectiveness in data-scarce sensing modalities.

    Conclusions:

    • The enhanced ADDA framework offers a simple yet efficient approach to unsupervised domain adaptation.
    • The novel loss functions and regularization techniques effectively improve performance and stability in UDA tasks.
    • The framework shows significant promise for real-world applications, particularly in domains with limited labeled data, such as neuromorphic sensing.