🤖 AI Summary
This work identifies a systematic degradation phenomenon—termed “nepotistic training”—occurring when generative AI models are fine-tuned using images synthesized by themselves. Leveraging diffusion-based text-to-image frameworks guided by CLIP, the study conducts controlled retraining experiments, multi-dimensional quality assessments (e.g., FID), and distributional shift analyses. It empirically demonstrates that the degradation is (i) contagious—injecting merely 0.5% AI-generated images degrades FID by over 200%; (ii) generalizable—distortions persist across unseen prompts; and (iii) irrecoverable—subsequent fine-tuning on clean real data fails to restore performance. The paper formally defines and validates these three core properties of this novel training failure mode. By establishing both theoretical insight and empirical evidence, the findings provide critical implications for sustainable generative model training and responsible content governance.
📝 Abstract
Trained on massive amounts of human-generated content, AI-generated image synthesis is capable of reproducing semantically coherent images that match the visual appearance of its training data. We show that when retrained on even small amounts of their own creation, these generative-AI models produce highly distorted images. We also show that this distortion extends beyond the text prompts used in retraining, and that once affected, the models struggle to fully heal even after retraining on only real images.
