16 citations · 65 across the 47 of their papers we have counts for
7 papers · 2 filters
Locality-Aware Continual Unlearning for Diffusion Models
Naveen George, Naoki Murata, Yuhta Takida +2
Real-world deployment of text-to-image diffusion models requires continual concept removal as new privacy, copyright, or safety obligations arise over time. Existing unlearning met…
Demystifying MaskGIT Sampler and Beyond: Adaptive Order Selection in Masked Diffusion
Satoshi Hayakawa, Yuhta Takida, Masaaki Imaizumi +2
Masked diffusion models have shown promising performance in generating high-quality samples in a wide range of domains, but accelerating their sampling process remains relatively u…
Theoretical Refinement of CLIP by Utilizing Linear Structure of Optimal Similarity
Naoki Yoshida, Satoshi Hayakawa, Yuhta Takida +3
In this study, we propose an enhancement to the similarity computation mechanism in multi-modal contrastive pretraining frameworks such as CLIP. Prior theoretical research has demo…
SONA: Learning Conditional, Unconditional, and Mismatching-Aware Discriminator
Yuhta Takida, Satoshi Hayakawa, Takashi Shibuya +6
Deep generative models have made significant advances in generating complex content, yet conditional generation remains a fundamental challenge. Existing conditional generative adv…
Denoising Multi-Beta VAE: Representation Learning for Disentanglement and Generation
Anshuk Uppal, Yuhta Takida, Chieh-Hsin Lai +1
Disentangled and interpretable latent representations in generative models typically come at the cost of generation quality. The -VAE framework introduces a hyperparameter t…
VCT: Training Consistency Models with Variational Noise Coupling
Gianluigi Silvestri, Luca Ambrogioni, Chieh-Hsin Lai +2
Consistency Training (CT) has recently emerged as a strong alternative to diffusion models for image generation. However, non-distillation CT often suffers from high variance and i…