Search Results for author: Chieh-Hsin Lai

Found 22 papers, 9 papers with code

Understanding Multimodal Contrastive Learning Through Pointwise Mutual Information

no code implementations30 Apr 2024 Toshimitsu Uesaka, Taiji Suzuki, Yuhta Takida, Chieh-Hsin Lai, Naoki Murata, Yuki Mitsufuji

Multimodal representation learning to integrate different modalities, such as text, vision, and audio is important for real-world applications.

Classification Contrastive Learning +2

Manifold Preserving Guided Diffusion

no code implementations28 Nov 2023 Yutong He, Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim, Wei-Hsiang Liao, Yuki Mitsufuji, J. Zico Kolter, Ruslan Salakhutdinov, Stefano Ermon

Despite the recent advancements, conditional image generation still faces challenges of cost, generalizability, and the need for task-specific training.

Conditional Image Generation

Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion

1 code implementation1 Oct 2023 Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, Stefano Ermon

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.

Denoising Image Generation

On the Equivalence of Consistency-Type Models: Consistency Models, Consistent Diffusion Models, and Fokker-Planck Regularization

no code implementations1 Jun 2023 Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Naoki Murata, Yuki Mitsufuji, Stefano Ermon

The emergence of various notions of ``consistency'' in diffusion models has garnered considerable attention and helped achieve improved sample quality, likelihood estimation, and accelerated sampling.

SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer

1 code implementation30 Jan 2023 Yuhta Takida, Masaaki Imaizumi, Takashi Shibuya, Chieh-Hsin Lai, Toshimitsu Uesaka, Naoki Murata, Yuki Mitsufuji

Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives.

Image Generation

GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration

1 code implementation30 Jan 2023 Naoki Murata, Koichi Saito, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon

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.

Blind Image Deblurring Denoising +1

Unsupervised vocal dereverberation with diffusion-based generative models

no code implementations8 Nov 2022 Koichi Saito, Naoki Murata, Toshimitsu Uesaka, Chieh-Hsin Lai, Yuhta Takida, Takao Fukui, Yuki Mitsufuji

Removing reverb from reverberant music is a necessary technique to clean up audio for downstream music manipulations.

FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation

1 code implementation9 Oct 2022 Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon

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.

Denoising

SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization

1 code implementation16 May 2022 Yuhta Takida, Takashi Shibuya, WeiHsiang Liao, Chieh-Hsin Lai, Junki Ohmura, Toshimitsu Uesaka, Naoki Murata, Shusuke Takahashi, Toshiyuki Kumakura, Yuki Mitsufuji

In this paper, we propose a new training scheme that extends the standard VAE via novel stochastic dequantization and quantization, called stochastically quantized variational autoencoder (SQ-VAE).

Quantization

Robust Vector Quantized-Variational Autoencoder

no code implementations4 Feb 2022 Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman

We experimentally demonstrate that RVQ-VAE is able to generate examples from inliers even if a large portion of the training data points are corrupted.

Decoder Quantization

Preventing Oversmoothing in VAE via Generalized Variance Parameterization

no code implementations17 Feb 2021 Yuhta Takida, Wei-Hsiang Liao, Chieh-Hsin Lai, Toshimitsu Uesaka, Shusuke Takahashi, Yuki Mitsufuji

Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon in which the learned latent space becomes uninformative.

Decoder

Novelty Detection via Robust Variational Autoencoding

1 code implementation9 Jun 2020 Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman

We establish both robustness to outliers and suitability to low-rank modeling of the Wasserstein metric as opposed to the KL divergence.

Dimensionality Reduction Novelty Detection

Inverse Problems, Deep Learning, and Symmetry Breaking

no code implementations20 Mar 2020 Kshitij Tayal, Chieh-Hsin Lai, Vipin Kumar, Ju Sun

In many physical systems, inputs related by intrinsic system symmetries are mapped to the same output.

Retrieval

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