no code implementations • 5 May 2024 • Seojin Kim, Jaehyun Nam, Sihyun Yu, Younghoon Shin, Jinwoo Shin
Compared to the conventional textual inversion method in the image domain using a single-level token embedding, our multi-level token embeddings allow the model to effectively learn the underlying low-shot molecule distribution.
no code implementations • 21 Mar 2024 • Sihyun Yu, Weili Nie, De-An Huang, Boyi Li, Jinwoo Shin, Anima Anandkumar
To tackle this issue, we propose content-motion latent diffusion model (CMD), a novel efficient extension of pretrained image diffusion models for video generation.
1 code implementation • CVPR 2023 • Jongheon Jeong, Sihyun Yu, Hankook Lee, Jinwoo Shin
In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition (i. e., less generalizable), so that one cannot prevent a model from co-adapting on such (so-called) "shortcut" signals: this makes the model fragile in various distribution shifts.
1 code implementation • CVPR 2023 • Sihyun Yu, Kihyuk Sohn, Subin Kim, Jinwoo Shin
Specifically, PVDM is composed of two components: (a) an autoencoder that projects a given video as 2D-shaped latent vectors that factorize the complex cubic structure of video pixels and (b) a diffusion model architecture specialized for our new factorized latent space and the training/sampling procedure to synthesize videos of arbitrary length with a single model.
1 code implementation • 13 Oct 2022 • Subin Kim, Sihyun Yu, Jaeho Lee, Jinwoo Shin
Succinct representation of complex signals using coordinate-based neural representations (CNRs) has seen great progress, and several recent efforts focus on extending them for handling videos.
1 code implementation • ICLR 2022 • Sihyun Yu, Jihoon Tack, Sangwoo Mo, Hyunsu Kim, Junho Kim, Jung-Woo Ha, Jinwoo Shin
In this paper, we found that the recent emerging paradigm of implicit neural representations (INRs) that encodes a continuous signal into a parameterized neural network effectively mitigates the issue.
Ranked #25 on Video Generation on UCF-101
no code implementations • NeurIPS 2021 • Sihyun Yu, Sungsoo Ahn, Le Song, Jinwoo Shin
We consider the problem of searching an input maximizing a black-box objective function given a static dataset of input-output queries.
no code implementations • 22 Jul 2021 • Sihyun Yu, Sangwoo Mo, Sungsoo Ahn, Jinwoo Shin
Abstract reasoning, i. e., inferring complicated patterns from given observations, is a central building block of artificial general intelligence.
1 code implementation • ICML Workshop AML 2021 • Jihoon Tack, Sihyun Yu, Jongheon Jeong, Minseon Kim, Sung Ju Hwang, Jinwoo Shin
Adversarial training (AT) is currently one of the most successful methods to obtain the adversarial robustness of deep neural networks.