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- MACE: Mass Concept Erasure in Diffusion Models370
This paper introduces MACE, a finetuning framework for the task of MAss Concept Erasure, which aims to prevent models from generating images that embody unwanted concepts when prompted by leveraging closed-form cross-attention refinement along with LoRA finetuning.
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- Variance-Reduction Guidance: Sampling Trajectory Optimization for Diffusion Models2
A novel technique for statistically measuring the prediction error is introduced and the Variance-Reduction Guidance (VRG) method is proposed, which can significantly improve the generation quality of diffusion models.
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- Enhancing Bioactivity Prediction via Spatial Emptiness Representation of Protein-ligand Complex and Union of Multiple Pockets1
LigoSpace introduces GeoREC to quantify atomic-level empty space and Union-Pocket to unify multiple protein pockets, providing a global view of binding sites and employs a pairwise loss instead of commonly used MSE loss, to better capture relative relationships critical for drug discovery.
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- FVeinSyn: Synthetic Finger Vein Image Generator–
FVeinSyn explicitly decouples synthesis of vascular topology and imaging appearance to mitigate the limitations caused by insufficient training samples, such as inadequate identity diversity and restricted realism.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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