LA-LQR applies latent-space linear-quadratic regulator control to steer text-to-video model activations toward desired features while penalizing excessive changes.
Unlearning concepts from text-to-video diffusion models,
2 Pith papers cite this work. Polarity classification is still indexing.
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The paper identifies a concept-layer topological alignment bottleneck in text-to-video diffusion models and introduces the CLEAR separability-driven optimization framework for targeted concept erasure.
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Where Concept Erasure Should Occur: Concept-Layer Alignment in Text-to-Video Diffusion Models
The paper identifies a concept-layer topological alignment bottleneck in text-to-video diffusion models and introduces the CLEAR separability-driven optimization framework for targeted concept erasure.