no code implementations • 12 Dec 2023 • Yoonwoo Jeong, Jinwoo Lee, Chiheon Kim, Minsu Cho, Doyup Lee
Transfer learning of large-scale Text-to-Image (T2I) models has recently shown impressive potential for Novel View Synthesis (NVS) of diverse objects from a single image.
1 code implementation • CVPR 2023 • Jaehoon Yoo, Semin Kim, Doyup Lee, Chiheon Kim, Seunghoon Hong
However, the transformers are prohibited from directly learning the long-term dependency in videos due to the quadratic complexity of self-attention, and inherently suffering from slow inference time and error propagation due to the autoregressive process.
Ranked #25 on Video Generation on UCF-101
1 code implementation • CVPR 2023 • Chiheon Kim, Doyup Lee, Saehoon Kim, Minsu Cho, Wook-Shin Han
Despite recent advances in implicit neural representations (INRs), it remains challenging for a coordinate-based multi-layer perceptron (MLP) of INRs to learn a common representation across data instances and generalize it for unseen instances.
no code implementations • 9 Jun 2022 • Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, Wook-Shin Han
After code stacks in the sequence are randomly masked, Contextual RQ-Transformer is trained to infill the masked code stacks based on the unmasked contexts of the image.
Ranked #1 on Text-to-Image Generation on Conceptual Captions
3 code implementations • CVPR 2022 • Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, Wook-Shin Han
However, we postulate that previous VQ cannot shorten the code sequence and generate high-fidelity images together in terms of the rate-distortion trade-off.
Ranked #2 on Text-to-Image Generation on Conceptual Captions
1 code implementation • ICML Workshop AutoML 2021 • Chiheon Kim, Saehoon Kim, Jongmin Kim, Donghoon Lee, Sungwoong Kim
Large-batch training has been essential in leveraging large-scale datasets and models in deep learning.
3 code implementations • 21 Apr 2020 • Chiheon Kim, Heungsub Lee, Myungryong Jeong, Woonhyuk Baek, Boogeon Yoon, Ildoo Kim, Sungbin Lim, Sungwoong Kim
We design and implement a ready-to-use library in PyTorch for performing micro-batch pipeline parallelism with checkpointing proposed by GPipe (Huang et al., 2019).
1 code implementation • NeurIPS 2019 • Sangwoo Mo, Chiheon Kim, Sungwoong Kim, Minsu Cho, Jinwoo Shin
Conditional generative adversarial networks (cGANs) have gained a considerable attention in recent years due to its class-wise controllability and superior quality for complex generation tasks.
no code implementations • 13 Jun 2019 • Sungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, Taesup Kim
In this paper, a neural architecture search (NAS) framework is proposed for 3D medical image segmentation, to automatically optimize a neural architecture from a large design space.
11 code implementations • NeurIPS 2019 • Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, Sungwoong Kim
Data augmentation is an essential technique for improving generalization ability of deep learning models.
Ranked #2 on Image Classification on SVHN
no code implementations • 8 Jul 2018 • Chiheon Kim, Afonso S. Bandeira, Michel X. Goemans
We study the problem of community detection in a random hypergraph model which we call the stochastic block model for $k$-uniform hypergraphs ($k$-SBM).