Multi-Modal Gradual Domain Osmosis: Stepwise Dynamic Learning with Batch Matching for Gradual Domain Adaptation

In this paper, we propose a new method called Multi-Modal Gradual Domain Osmosis, which aims to solve the problem of smooth knowledge migration from the source domain to the target domain in Gradual Domain Adaptation (GDA). Traditional Gradual Domain Adaptation methods mitigate domain bias by introducing intermediate domains and self-training strategies but often face the challenges of inefficient knowledge migration or missing data in intermediate domains. In this paper, we design an optimization framework based on the hyperparameter łambda by dynamically balancing the loss weights of the source and target domains, which enables the model to progressively adjust the strength of knowledge migration (łambda incrementing from 0 to 1) during the training process, thus achieving cross-domain generalization more efficiently. Specifically, the method incorporates self-training to generate pseudo-labels and iteratively updates the model by minimizing a weighted loss function to ensure stability and robustness during progressive adaptation in the intermediate domain. The experimental part validates the effectiveness of the method on rotated MNIST, color-shifted MNIST, portrait dataset, and forest cover type dataset, and the results show that it outperforms existing baseline methods. The paper further analyses the impact of the dynamic tuning strategy of the hyperparameter łambda on the performance through ablation experiments, confirming the advantages of progressive domain penetration in mitigating domain bias and enhancing the model generalization capability. The study provides theoretical support and a practical framework for asymptotic domain adaptation and expands its application potential in dynamic environments.