AD-Aligning: Emulating Human-like Generalization for Cognitive Domain Adaptation in Deep Learning
<|reference_start|>AD-Aligning: Emulating Human-like Generalization for Cognitive Domain Adaptation in Deep Learning: Domain adaptation is pivotal for enabling deep learning models to generalize across diverse domains, a task complicated by variations in presentation and cognitive nuances. In this paper, we introduce AD-Aligning, a novel approach that combines adversarial training with source-target domain alignment to enhance generalization capabilities. By pretraining with Coral loss and standard loss, AD-Aligning aligns target domain statistics with those of the pretrained encoder, preserving robustness while accommodating domain shifts. Through extensive experiments on diverse datasets and domain shift scenarios, including noise-induced shifts and cognitive domain adaptation tasks, we demonstrate AD-Aligning's superior performance compared to existing methods such as Deep Coral and ADDA. Our findings highlight AD-Aligning's ability to emulate the nuanced cognitive processes inherent in human perception, making it a promising solution for real-world applications requiring adaptable and robust domain adaptation strategies.<|reference_end|>
@article{li2024ad-aligning:,
    title={AD-Aligning: Emulating Human-like Generalization for Cognitive Domain
  Adaptation in Deep Learning},
    author={Zhuoying Li, Bohua Wan, Cong Mu, Ruzhang Zhao, Shushan Qiu, and Chao
  Yan},
    journal={arXiv preprint arXiv:2405.09582},
    year={2024},
    doi={10.1109/ICECAI62591.2024.10675013},
    archivePrefix={arXiv},
    eprint={2405.09582},
    primaryClass={cs.CV eess.IV}
}