Fuzzy Domain Adaptation via Variational Inference for Evolving Concept Drift

The concept of fuzzy domain adaptation (FDA) is focused on transferring a model trained in a source domain to a target domain, where intrinsic distribution discrepancies exist in non-stationary and non-deterministic environments. In this paper, a novel drift decoupling-based variational adaptation network (DD-VAN) is proposed for FDA, allowing for the learning of intra-domain evolutionary patterns and inter-domain uncertainties. The DD-VAN algorithm is implemented in three main steps: (1) an intra-domain evolutionary trend modeling module is first employed to capture unknown temporal variations through an autoencoder architecture with variational inference; (2) a prototype-assisted fuzzy clustering module is used to estimate the membership degree of the target data, characterizing the inherent uncertainty and imprecision present in real-world distributions; and (3) a membership-aware domain fuzzy matching module is utilized to learn the gradual transitions between category-related data pairs in the source and target domains by introducing uncertainties. Furthermore, it is theoretically demonstrated that the inferred posterior distributions of latent codes can be optimized to align with the corresponding prior distributions by minimizing the Kullback-Leibler divergence. Extensive experiments are conducted on cross-domain tasks involving both synthetic and realworld datasets, and the experimental results suggest that the DDVAN algorithm outperforms existing state-of-the-art methods.