Abstract: To improve the performance of deep learning models on an unbalanced dataset of small samples of laser welded surface defects, an adversarial generative network (GAN) model using small datasets as input is optimized. By comparing the difference in feature complexity between laser welding defects and other public datasets used to test adversarial generative networks, a new OCM (one class mixup) module is designed and introduced into the stylegan2-ada for a limited number of samples to improve the performance of the adversarial generative network and accelerate its convergence. The results show that the dataset generated by OCM-stylegan2-ada improves the performance of the classification model by 40% over the original dataset and by 20% over the dataset enhanced with mixup and stylegan2-ada. Also the quality of the visually generated images of weld defects is greatly improved.