Energy-Driven Explicit Alignment Network: A Blended-Target Domain Adaptation Approach

As a specialized paradigm of domain adaptation, blended-target domain adaptation (BTDA) transfers knowledge from a source domain to a blended target domain. In this paper, we propose an Energy-Driven Explicit Alignment Network (EDEAN) framework that innovatively applies energy-based models (EBMs) to address BTDA problems. We observe that EBMs display free energy biases when the source domain and the target domain data originate from different distributions. Therefore, we use these biases as a measure of the discrepancies between the source domain and the target domain and align them by minimizing these biases via the free energy alignment (FEA) module. We further propose the balanced weight distribution (BWD) module, which comprehensively considers the complementary information between the linear and semantic pseudo-labels and obtains the corresponding complementary information by mixing both label types. Moreover, we propose the normalized free energy (NFE) module, which assigns higher weights to high free energy samples and dynamically corrects the pseudo-labels by continuously updating these weights. We also conducted experiments on four widely used BTDA databases and achieved substantial improvements over the latest BTDA methods.