Harnessing Diffusion-Generated Synthetic Images for Fair Image Classification

The study titled 'Harnessing Diffusion-Generated Synthetic Images for Fair Image Classification' highlights the persistent issue of bias in image classification systems, which often arises from uneven representation in training datasets. For instance, datasets may disproportionately associate certain traits, like hair color, with specific genders, reinforcing stereotypes. To combat this, the researchers leverage the Stable Diffusion model alongside finetuning techniques such as LoRA and DreamBooth. They introduce a novel methodology that clusters images within each group, allowing for the generation of more representative training data. Their experiments demonstrate that these finetuning approaches not only outperform the standard Stable Diffusion model but also yield results comparable to state-of-the-art debiasing techniques. This work is crucial as it paves the way for more equitable AI systems, ensuring that image classification reflects a fairer representation of diverse populatio…