Improving Generative Model Self-Training with Geometrically Modified Outputs

The paper introduces Geometrically Modified Outputs (GMOs), a technique that reweights the singular values of a generative model’s input-output Jacobian to strengthen the negative signal used in self‑training. By amplifying mode‑seeking behavior and distortions in standard outputs, GMOs provide a more targeted negative guidance for models such as Neon and SIMS. Experiments on one‑step generative models show that GMOs consistently improve the performance of negative‑guidance self‑training methods compared to using unmodified outputs.

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• fine-tuning