Adaptive Neural Network Architectures for Cross-Domain Generalization

Abstract

Cross-domain generalization remains a critical challenge in the field of machine learning. Traditional models often struggle to maintain performance when applied to new, unseen domains due to the variations in data distribution, known as domain shift. This paper proposes adaptive neural network architectures that dynamically adjust their structure based on the domain of the input data. Our approach leverages dynamic routing, attention mechanisms, and modular neural networks to enhance the model's adaptability and robustness. The dynamic routing mechanism enables the network to select different paths for different inputs, allowing it to adapt its processing dynamically. Attention mechanisms help the model focus on the most relevant parts of the input data, enhancing its ability to generalize across domains. Modular neural networks consist of multiple independent modules that can be selectively activated or deactivated based on the input domain. We also develop a dynamic adaptation mechanism that adjusts the network structure in real-time based on domain-specific input features. Experimental results on multiple benchmark datasets, including NEU-CLS and Lithium Electronic Surface Defect Classification (IESDC) datasets, demonstrate the effectiveness of our method. The proposed approach shows significant improvements in cross-domain performance compared to state-of-the-art models, achieving higher accuracy and robustness. Ablation studies confirm the contribution of each component to the overall performance enhancement. The findings highlight the potential of adaptive architectures in addressing the challenges of domain shift in machine learning applications.