GIDDM: Generating Labels with Diffusion Model to Promote Cross-domain Open-set Image Recognition

Due to the lack of prior knowledge about unknown classes during training, existing methods for cross-domain open-set image recognition typically rely on threshold-based solutions. However, such approaches often struggle to capture the complex boundary relationships between known and unknown classes, which can lead to negative transfer effects caused by feature confusion between the two. To address this issue, this paper proposes a graph isomorphic distillation diffusion model (GIDDM) that aims to learn the boundary relationships between known and unknown classes from a closed-set classifier that models predictive uncertainty. First, a diffusion classifier is designed to quantify model predictive uncertainty through a Monte Carlo sampling strategy performed on the noise distribution during the reverse denoising process. The uncertainty distribution is modeled, and the cumulative distribution function is used to compute the probability of a sample belonging to an unknown class. Second, an open-set recognition framework is constructed, treating the closed-set diffusion classifier as a teacher classifier, and guiding the student classifier to learn the complex boundary relationships between known and unknown classes through knowledge distillation. Third, the knowledge distillation process is further formalized as a graph isomorphic optimization problem, where the predictive manifolds of the student and teacher classifiers are constrained to be consistent, thereby enhancing knowledge transfer between the classifiers. Finally, the entire process is integrated into a unified open-set adversarial domain adaptation framework, reconstructing the traditional optimization objectives of closed-set adversarial domain adaptation to ensure sufficient separation between known and unknown classes while aligning the distributions of known classes in both the source and target domains. Experiments conducted on multiple hyperspectral image (HSI) datasets demonstrate that the proposed method achieves state-of-the-art performance on cross-domain open-set image recognition tasks. The code demo can be accessed on the following website: https://github.com/wzr78998/GIDDM.