26 citations · 129 across the 44 of their papers we have counts for
29 papers · 1 filter
Learning What to Recall: Adaptive Multi-Cue Episodic Memory for World Models
Beomsu Kim, Chieh-Hsin Lai, Bac Nguyen +3
World models predict future observations from current experience and actions, yet prediction can depend on observations seen far in the past. Episodic memory preserves past observa…
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…
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…
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…
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…
The Principles of Diffusion Models
Chieh-Hsin Lai, Yang Song, Dongjun Kim +2
This book presents the core principles that have guided the development of diffusion models, tracing their origins and showing how diverse formulations arise from shared mathematic…
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…
Improving Classifier-Free Guidance in Masked Diffusion: Low-Dim Theoretical Insights with High-Dim Impact
Kevin Rojas, Ye He, Chieh-Hsin Lai +3
Classifier-Free Guidance (CFG) is a widely used technique for conditional generation and improving sample quality in continuous diffusion models, and its extensions to discrete dif…
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…
: Optimizing Sampling Schedule of Discrete Diffusion Models
Yong-Hyun Park, Chieh-Hsin Lai, Satoshi Hayakawa +2
Diffusion models have seen notable success in continuous domains, leading to the development of discrete diffusion models (DDMs) for discrete variables. Despite recent advances, DD…
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space
Yangming Li, Chieh-Hsin Lai, Carola-Bibiane Schönlieb +2
Deep Generative Models (DGMs), including Energy-Based Models (EBMs) and Score-based Generative Models (SGMs), have advanced high-fidelity data generation and complex continuous dis…
Improving Vector-Quantized Image Modeling with Latent Consistency-Matching Diffusion
Bac Nguyen, Chieh-Hsin Lai, Yuhta Takida +4
By embedding discrete representations into a continuous latent space, we can leverage continuous-space latent diffusion models to handle generative modeling of discrete data. Howev…
HERO: Human-Feedback Efficient Reinforcement Learning for Online Diffusion Model Finetuning
Ayano Hiranaka, Shang-Fu Chen, Chieh-Hsin Lai +6
Controllable generation through Stable Diffusion (SD) fine-tuning aims to improve fidelity, safety, and alignment with human guidance. Existing reinforcement learning from human fe…
A Survey on Diffusion Models for Inverse Problems
Giannis Daras, Hyungjin Chung, Chieh-Hsin Lai +5
Diffusion models have become increasingly popular for generative modeling due to their ability to generate high-quality samples. This has unlocked exciting new possibilities for so…
Weighted Point Set Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric
Toshimitsu Uesaka, Taiji Suzuki, Yuhta Takida +3
In typical multimodal contrastive learning, such as CLIP, encoders produce one point in the latent representation space for each input. However, one-point representation has diffic…
HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes
Yuhta Takida, Yukara Ikemiya, Takashi Shibuya +8
Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations. It is commonly performed with a variational autoencoding model,…
Manifold Preserving Guided Diffusion
Yutong He, Naoki Murata, Chieh-Hsin Lai +8
Despite the recent advancements, conditional image generation still faces challenges of cost, generalizability, and the need for task-specific training. In this paper, we propose M…
Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion
Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao +6
Consistency Models (CM) (Song et al., 2023) accelerate score-based diffusion model sampling at the cost of sample quality but lack a natural way to trade-off quality for speed. To…
On the Equivalence of Consistency-Type Models: Consistency Models, Consistent Diffusion Models, and Fokker-Planck Regularization
Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka +3
The emergence of various notions of ``consistency'' in diffusion models has garnered considerable attention and helped achieve improved sample quality, likelihood estimation, and a…
SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer
Yuhta Takida, Masaaki Imaizumi, Takashi Shibuya +4
Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives. This paper addresses the quest…
GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration
Naoki Murata, Koichi Saito, Chieh-Hsin Lai +4
Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements…
FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation
Chieh-Hsin Lai, Yuhta Takida, Naoki Murata +3
Score-based generative models (SGMs) learn a family of noise-conditional score functions corresponding to the data density perturbed with increasingly large amounts of noise. These…
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization
Yuhta Takida, Takashi Shibuya, WeiHsiang Liao +7
One noted issue of vector-quantized variational autoencoder (VQ-VAE) is that the learned discrete representation uses only a fraction of the full capacity of the codebook, also kno…