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cs.CV2026
Consistency-Preserving Concept Erasure via Unsafe-Safe Pairing and Directional Fisher-weighted Adaptation
Yongwoo Kim, Sungmin Cha, Hyunsoo Kim +2
With the increasing versatility of text-to-image diffusion models, the ability to selectively erase undesirable concepts (e.g., harmful content) has become indispensable. However,…
cs.CV2024
Improving Generative Pre-Training: An In-depth Study of Masked Image Modeling and Denoising Models
Hyesong Choi, Daeun Kim, Sungmin Cha +2
In this work, we dive deep into the impact of additive noise in pre-training deep networks. While various methods have attempted to use additive noise inspired by the success of la…
cs.CV2024
Salience-Based Adaptive Masking: Revisiting Token Dynamics for Enhanced Pre-training
Hyesong Choi, Hyejin Park, Kwang Moo Yi +2
In this paper, we introduce Saliency-Based Adaptive Masking (SBAM), a novel and cost-effective approach that significantly enhances the pre-training performance of Masked Image Mod…