Geometric Erasure by Contrastive Velocity Matching in Rectified Flows
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914619416641536 |
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| author | Grebe, Jonas Henry Braun, Tobias Rohrbach, Anna Rohrbach, Marcus |
| author_facet | Grebe, Jonas Henry Braun, Tobias Rohrbach, Anna Rohrbach, Marcus |
| contents | While the rapid adoption of multimodal generative models offers immense potential, it has also increased the risks of harmful content synthesis, deepfakes, and copyright infringements. To address these challenges, concept erasure has emerged as a prospective safeguard. However, as the field gradually transitions from U-Net-based diffusion models to Rectified Flow Transformers, erasure research has struggled to keep pace. In this work, we introduce GEM, a simple but highly effective erasure framework for Rectified Flow models. As part of our contribution, we establish a principled bridge between trajectory-based unlearning grounded in Generative Flow Networks and classic teacher-guided erasure: we translate trajectory-based signals into a teacher-guided flow-matching setup that unifies the strengths of both paradigms. Concretely, a teacher provides complementary attraction and repulsion signals that we combine into a single geometric guidance objective, yielding targeted suppression of unwanted concepts while preserving benign generation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2606_00140 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Geometric Erasure by Contrastive Velocity Matching in Rectified Flows Grebe, Jonas Henry Braun, Tobias Rohrbach, Anna Rohrbach, Marcus Machine Learning Artificial Intelligence While the rapid adoption of multimodal generative models offers immense potential, it has also increased the risks of harmful content synthesis, deepfakes, and copyright infringements. To address these challenges, concept erasure has emerged as a prospective safeguard. However, as the field gradually transitions from U-Net-based diffusion models to Rectified Flow Transformers, erasure research has struggled to keep pace. In this work, we introduce GEM, a simple but highly effective erasure framework for Rectified Flow models. As part of our contribution, we establish a principled bridge between trajectory-based unlearning grounded in Generative Flow Networks and classic teacher-guided erasure: we translate trajectory-based signals into a teacher-guided flow-matching setup that unifies the strengths of both paradigms. Concretely, a teacher provides complementary attraction and repulsion signals that we combine into a single geometric guidance objective, yielding targeted suppression of unwanted concepts while preserving benign generation. |
| title | Geometric Erasure by Contrastive Velocity Matching in Rectified Flows |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2606.00140 |