Improving Generative Model Self-Training with Geometrically Modified Outputs

2026-09-28

AI Summary

This study addresses the performance degradation caused by model collapse during the self-training of generative models. To mitigate this issue, we propose a geometric correction method based on singular value reweighting of the Jacobian matrix. This approach explicitly reinforces negative guidance signals for the first time by amplifying mode-seeking behavior and distortion characteristics, thereby overcoming the limitations of conventional direct fine-tuning. When integrated with algorithms such as Neon and SIMS, the proposed technique significantly enhances both self-training performance and iterative stability across various one-step generative models, particularly in data-scarce scenarios.

Abstract

Self-training generative models - the continued improvement of a model using its own outputs - is becoming increasingly important as high-quality training data becomes scarce. However, naively finetuning on model-generated samples leads to degradation through model collapse and the model autophagy disorder. Negative-guidance self-training methods turn this degradation into a useful signal, using a model finetuned on its own outputs to guide the original model toward improved generation. Existing methods, however, take the negative signal in standard model outputs as given. We instead ask whether this signal can be explicitly strengthened. We introduce Geometrically Modified Outputs (GMOs), which reweight the singular values of the generator's input-output Jacobian to increase the influence of its leading singular directions. This geometric modification amplifies the mode-seeking behavior and distortions of standard outputs, providing a stronger and more targeted negative signal for self-training. Across a range of one-step generative models, GMOs consistently improve the performance of negative-guidance methods, including Neon and SIMS, compared with using standard model outputs.

Problem

• Generative Model
• Self-Training
• Model Collapse
• Negative Guidance

Innovation

• Self-training
• Generative models
• Geometrically Modified Outputs
• Negative guidance
• Jacobian singular values