16 citations · 65 across the 47 of their papers we have counts for
50 papers
Does Uniform Discrete Diffusion Need Time?
Chunsan Hong, Chieh-Hsin Lai, Satoshi Hayakawa +3
Uniform discrete diffusion models (UDMs) commonly use explicit time conditioning, but we find that it can often be unnecessary in practice. In this paper, we first show that the po…
Spectral Prior for Reducing Exposure Bias in Diffusion Models
Yuya Kobayashi, Masato Ishii, Yuhta Takida +2
Diffusion models typically suffer from error accumulation during iterative sampling, commonly referred to as exposure bias. We reveal systematic frequency-dependent discrepancies b…
From Atoms to Entropy: Optimal Noise Allocation for Diffusion Training in the Convex Regime
Luca Ambrogioni, Giulio Franzese, Alberto Foresti +7
How should a diffusion model decide which noise levels to train on, and how much? Despite the importance of this choice, current noise schedules are based largely on heuristics or…
TILDE: TILt-based Distributional Erasure for Concept Unlearning
Naveen George, Naoki Murata, Yuhta Takida +2
Concept unlearning in text-to-image diffusion models is critical for safe and practical deployment: with rising privacy concerns, copyright disputes, trademark constraints, and saf…
Spatio-Temporal Audio Language Modeling for Dynamic Sound Sources
Oh Hyun-Bin, Kazuki Shimada, Yuhta Takida +6
Sound events are entities with semantic identities, locations, and trajectories, but current audio-language models usually reason about clips as global event content. Conversely, s…
Efficient Reinforcement for Visual-Textual Thinking with Discrete Diffusion Model
Yoonjeon Kim, Yuhta Takida, Chieh-Hsin Lai +2
RL-based post-training has been widely adopted to enable interleaved visual and textual reasoning in unified multimodal models capable of both text and image generation. However, m…
Understanding and Accelerating the Training of Masked Diffusion Language Models
Chunsan Hong, Sanghyun Lee, Chieh-Hsin Lai +5
Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models (ARMs) for language modeling. However, MDMs are known to learn substantially more sl…
A Unified View of Score-Based and Drifting Models
Chieh-Hsin Lai, Bac Nguyen, Naoki Murata +5
Drifting models train one-step generators by optimizing a kernel-induced mean-shift discrepancy between the data and model distributions, with Laplace kernels used by default in pr…
Improved Object-Centric Diffusion Learning with Registers and Contrastive Alignment
Bac Nguyen, Yuhta Takida, Naoki Murata +4
Slot Attention (SA) with pretrained diffusion models has recently shown promise for object-centric learning (OCL), but suffers from slot entanglement and weak alignment between obj…
Noise Scheduling as Information-Guided Allocation in Diffusion Training
Gabriel Raya, Bac Nguyen, Georgios Batzolis +6
We introduce InfoNoise, an online adaptive noise schedule for diffusion training that reallocates optimization effort toward noise levels where denoising is most informative. Toget…
GUDA: Counterfactual Group-wise Training Data Attribution for Diffusion Models via Unlearning
Naoki Murata, Yuhta Takida, Chieh-Hsin Lai +4
Training-data attribution for vision generative models aims to identify which training data influenced a given output. While most methods score individual examples, practitioners o…
PAVAS: Physics-Aware Video-to-Audio Synthesis
Oh Hyun-Bin, Yuhta Takida, Toshimitsu Uesaka +2
Recent advances in Video-to-Audio (V2A) generation have achieved impressive perceptual quality and temporal synchronization, yet most models remain appearance-driven, capturing vis…
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 multimodal contrastive pretraining frameworks such as CLIP. Prior theoretical research has demon…
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…
Concept-TRAK: Understanding how diffusion models learn concepts through concept-level attribution
Yonghyun Park, Chieh-Hsin Lai, Satoshi Hayakawa +7
While diffusion models excel at image generation, their growing adoption raises critical concerns about copyright issues and model transparency. Existing attribution methods identi…
Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity
Yuya Kobayashi, Yuhta Takida, Takashi Shibuya +1
Recently, Generative Adversarial Networks (GANs) have been successfully scaled to billion-scale large text-to-image datasets. However, training such models entails a high training…
Large-Scale Training Data Attribution for Music Generative Models via Unlearning
Woosung Choi, Junghyun Koo, Kin Wai Cheuk +7
This paper explores the use of unlearning methods for training data attribution (TDA) in music generative models trained on large-scale datasets. TDA aims to identify which specifi…
Forging and Removing Latent-Noise Diffusion Watermarks Using a Single Image
Anubhav Jain, Yuya Kobayashi, Naoki Murata +6
Watermarking techniques are vital for protecting intellectual property and preventing fraudulent use of media. Most previous watermarking schemes designed for diffusion models embe…
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…
Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models
Zerui Tao, Yuhta Takida, Naoki Murata +2
Parameter-Efficient Fine-Tuning (PEFT) of text-to-image models has become an increasingly popular technique with many applications. Among the various PEFT methods, Low-Rank Adaptat…
Rethinking Negative Prompting for Concept Suppression: Unconditional Anchoring and Localized Guidance
Anubhav Jain, Yuya Kobayashi, Takashi Shibuya +4
Text-to-image diffusion models have rapidly transitioned from research prototypes to widely deployed tools. Along with their impressive capabilities, these models can also generate…