Self-attention embedded StyleGAN for virtual sample generation in sensing applications

Given the challenges of low variability in industrial processes, which intensify data scarcity and produce anomalous distributions that compromise data-driven model accuracy. Existing sample generation methods often overlook key factors such as sparsity and correlation among data. To address these challenges, this paper proposes a StyleGAN-based virtual sample generation method with an embedded self-attention mechanism (SASG-VSG). Firstly, StyleGAN is used to map the original data space to a disentangled latent space. The output variables then act as control conditions, guiding the model to interpolate along the output dimension to ensure a more uniform distribution of generated samples. Besides, a self-attention module is incorporated into the discriminator to enhance its ability to capture the similarity between the virtual samples and the original data distribution. Finally, validation experiments on a purified terephthalic acid (PTA) solvent system and a sulfur recovery unit (SRU) confirm the capability of the proposed SASG-VSG in generating high-quality virtual samples for soft-sensing applications.