Title: Mistake-driven Image Classification with FastGAN and SpinalNet. Abstract: Image classification with classes of varying difficulty can cause performance disparity in deep learning models and reduce the overall performance and reliability of the predictions. In this paper, we address the problem of imbalanced performance in image classification, where the trained model has performance deficits in some of the dataset's classes. By employing Generative Adversarial Networks (GANs) to augment these deficit classes, we finetune the model towards a balanced performance among the different classes and an overall better performance on the whole dataset. Specifically, we combine a light-weight GAN method, FastGAN (Liu et al., 2021), for class-wise data augmentation with Progressive SpinalNet (Chopra, 2021) and Sharpness-Aware Minimization (SAM) (Foret et al., 2020) for training. Unlike earlier works, during training, our method focuses on those classes with lowest accuracy after the initial training phase, which leads to better performance. Only these classes are augmented to boost the accuracy. Due to the use of a light-weight GAN method, the GAN-based augmentation is viable and effective for mistake-driven training even for datasets with only few images per class, while simultaneously requiring less computation than other, more complex GAN methods. Our extensive experiments, including ablation studies on all key components, show competitive or better accuracy than the previous state-of-the-art on five datasets with different sizes and image resolutions.