AD-Aligning: Emulating Human-like Generalization for Cognitive Domain Adaptation in Deep Learning

• URL: http://arxiv.org/abs/2405.09582v2
• Date: Tue, 21 May 2024 23:39:25 GMT
• Title: AD-Aligning: Emulating Human-like Generalization for Cognitive Domain Adaptation in Deep Learning
• Authors: Zhuoying Li, Bohua Wan, Cong Mu, Ruzhang Zhao, Shushan Qiu, Chao Yan,
• Abstract summary: Domain adaptation is pivotal for enabling deep learning models to generalize across diverse domains. We introduce AD-Aligning, a novel approach that combines adversarial training with source-target domain alignment. Our findings highlight AD-Aligning's ability to emulate the nuanced cognitive processes inherent in human perception.
• Score: 3.3543468626874486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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