16 citations · 57 across the 24 of their papers we have counts for
6 papers · 1 filter
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…
G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving
Naoki Murata, Chieh-Hsin Lai, Yuhta Takida +4
Recent literature has effectively leveraged diffusion models trained on continuous variables as priors for solving inverse problems. Notably, discrete diffusion models with discret…
DisMix: Disentangling Mixtures of Musical Instruments for Source-level Pitch and Timbre Manipulation
Yin-Jyun Luo, Kin Wai Cheuk, Woosung Choi +8
Existing work on pitch and timbre disentanglement has been mostly focused on single-instrument music audio, excluding the cases where multiple instruments are presented. To fill th…
PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher
Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao +5
The diffusion model performs remarkable in generating high-dimensional content but is computationally intensive, especially during training. We propose Progressive Growing of Diffu…
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,…