no code implementations • 30 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.
no code implementations • 31 Dec 2023 • Yuhta Takida, Yukara Ikemiya, Takashi Shibuya, Kazuki Shimada, Woosung Choi, Chieh-Hsin Lai, Naoki Murata, Toshimitsu Uesaka, Kengo Uchida, Wei-Hsiang Liao, Yuki Mitsufuji
Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations.
no code implementations • 28 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.
no code implementations • 20 Oct 2023 • Mengjie Zhao, Junya Ono, Zhi Zhong, Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Wei-Hsiang Liao, Takashi Shibuya, Hiromi Wakaki, Yuki Mitsufuji
Contrastive cross-modal models such as CLIP and CLAP aid various vision-language (VL) and audio-language (AL) tasks.
1 code implementation • 1 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.
Ranked #2 on Image Generation on CIFAR-10
no code implementations • 13 Sep 2023 • Carlos Hernandez-Olivan, Koichi Saito, Naoki Murata, Chieh-Hsin Lai, Marco A. Martínez-Ramirez, Wei-Hsiang Liao, Yuki Mitsufuji
Restoring degraded music signals is essential to enhance audio quality for downstream music manipulation.
2 code implementations • 14 Aug 2023 • Giorgio Fabbro, Stefan Uhlich, Chieh-Hsin Lai, Woosung Choi, Marco Martínez-Ramírez, WeiHsiang Liao, Igor Gadelha, Geraldo Ramos, Eddie Hsu, Hugo Rodrigues, Fabian-Robert Stöter, Alexandre Défossez, Yi Luo, Jianwei Yu, Dipam Chakraborty, Sharada Mohanty, Roman Solovyev, Alexander Stempkovskiy, Tatiana Habruseva, Nabarun Goswami, Tatsuya Harada, Minseok Kim, Jun Hyung Lee, Yuanliang Dong, Xinran Zhang, Jiafeng Liu, Yuki Mitsufuji
We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding.
no code implementations • 1 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.
1 code implementation • 30 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.
1 code implementation • 30 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.
Ranked #1 on Image Generation on FFHQ 1024 x 1024
no code implementations • 8 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.
1 code implementation • 9 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.
1 code implementation • 16 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).
no code implementations • 4 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.
no code implementations • 17 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.
no code implementations • 23 Oct 2020 • Kshitij Tayal, Chieh-Hsin Lai, Raunak Manekar, Zhong Zhuang, Vipin Kumar, Ju Sun
In many physical systems, inputs related by intrinsic system symmetries generate the same output.
1 code implementation • 9 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.
no code implementations • 20 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.
2 code implementations • ICLR 2020 • Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman
The encoder maps the data into a latent space, from which the RSR layer extracts the subspace.
Decoder Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly +4