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This paper benchmarks generative augmentation strategies for bias correction in fine-grained animal classification, comparing traditional transforms, FastGAN, and LoRA-tuned Stable Diffusion 1.5 on artificially imbalanced breeds from the Oxford-IIIT Pet Dataset. They find that FastGAN actively increases classifier bias in low-data regimes due to mode collapse, while Stable Diffusion with LoRA achieves the best performance, reducing the bias gap by 13.1% relative to the unaugmented baseline. The study identifies a sample-size boundary (20-50 images per class) below which GAN augmentation becomes detrimental.
FastGAN can backfire in low-data regimes, actively *increasing* classifier bias by over 20% due to mode collapse, a stark warning against blindly applying generative augmentation.
Generative models are widely used to compensate for class imbalance in AI training pipelines, yet their failure modes under low-data conditions are poorly understood. This paper reports a controlled benchmark comparing three augmentation strategies applied to a fine-grained animal classification task: traditional transforms, FastGAN, and Stable Diffusion 1.5 fine-tuned with Low-Rank Adaptation (LoRA). Using the Oxford-IIIT Pet Dataset with eight artificially underrepresented breeds, we find that FastGAN augmentation does not merely underperform at very low training set sizes but actively increases classifier bias, with a statistically significant large effect across three random seeds (bias gap increase: +20.7%, Cohen's d = +5.03, p = 0.013). The effect size here is large enough to give confidence in the direction of the finding despite the small number of seeds. Feature embedding analysis using t-distributed Stochastic Neighbor Embedding reveals that FastGAN images for severe-minority breeds form tight isolated clusters outside the real image distribution, a pattern consistent with mode collapse. Stable Diffusion with Low-Rank Adaptation produced the best results overall, achieving the highest macro F1 (0.9125 plus or minus 0.0047) and a 13.1% reduction in the bias gap relative to the unaugmented baseline. The data suggest a sample-size boundary somewhere between 20 and 50 training images per class below which GAN augmentation becomes harmful in this setting, though further work across additional domains is needed to establish where that boundary sits more precisely. All experiments run on a consumer-grade GPU with 6 to 8 GB of memory, with no cloud compute required.
