Search Results for author: Jongho Lee

Found 14 papers, 6 papers with code

A Note on LoRA

no code implementations7 Apr 2024 Vlad Fomenko, Han Yu, Jongho Lee, Stanley Hsieh, Weizhu Chen

LoRA (Low-Rank Adaptation) has emerged as a preferred method for efficiently adapting Large Language Models (LLMs) with remarkable simplicity and efficacy.

QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference

1 code implementation15 Feb 2024 Taesu Kim, Jongho Lee, Daehyun Ahn, Sarang Kim, Jiwoong Choi, Minkyu Kim, HyungJun Kim

We introduce QUICK, a group of novel optimized CUDA kernels for the efficient inference of quantized Large Language Models (LLMs).

Quantization

Fast and accurate sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularization

no code implementations4 Dec 2023 Heejun Shin, Taehee Kim, Jongho Lee, Se Young Chun, Seungryung Cho, Dongmyung Shin

In the FACT method, we meta-trained a neural network and a hash-encoder using a few scans (= 15), and a new regularization technique is utilized to reconstruct the details of an anatomical structure.

BlindHarmony: "Blind" Harmonization for MR Images via Flow model

1 code implementation ICCV 2023 Hwihun Jeong, Heejoon Byun, Dong Un Kang, Jongho Lee

These differences in images create a domain gap that needs to be bridged by a step called image harmonization, to process the images successfully using conventional or deep learning-based image analysis (e. g., segmentation).

Image Harmonization

Coil2Coil: Self-supervised MR image denoising using phased-array coil images

no code implementations16 Aug 2022 Juhyung Park, Dongwon Park, Hyeong-Geol Shin, Eun-Jung Choi, Hongjun An, Minjun Kim, Dongmyung Shin, Se Young Chun, Jongho Lee

Hence, methods such as Noise2Noise (N2N) that require only pairs of noise-corrupted images have been developed to reduce the burden of obtaining training datasets.

Image Denoising

Blocks-World Cameras

no code implementations CVPR 2021 Jongho Lee, Mohit Gupta

For several vision and robotics applications, 3D geometry of man-made environments such as indoor scenes can be represented with a small number of dominant planes.

DIFFnet: Diffusion parameter mapping network generalized for input diffusion gradient schemes and bvalues

1 code implementation4 Feb 2021 Juhung Park, Woojin Jung, Eun-Jung Choi, Se-Hong Oh, Dongmyung Shin, Hongjun An, Jongho Lee

In this study, a new deep neural network, referred to as DIFFnet, is developed to function as a generalized reconstruction tool of the diffusion-weighted signals for various gradient schemes and b-values.

DeepResp: Deep learning solution for respiration-induced B0 fluctuation artifacts in multi-slice GRE

no code implementations19 Jul 2020 Hongjun An, Hyeong-Geol Shin, Sooyoen Ji, Woojin Jung, SeHong Oh, Dongmyung Shin, Juhyung Park, Jongho Lee

DeepResp is designed to extract the respiration-induced phase errors from a complex image using deep neural networks.

Image and Video Processing

Stochastic Exposure Coding for Handling Multi-ToF-Camera Interference

no code implementations ICCV 2019 Jongho Lee, Mohit Gupta

As continuous-wave time-of-flight (C-ToF) cameras become popular in 3D imaging applications, they need to contend with the problem of multi-camera interference (MCI).

Artificial neural network for myelin water imaging

1 code implementation29 Apr 2019 Jieun Lee, Doohee Lee, Joon Yul Choi, Dongmyung Shin, Hyeong-Geol Shin, Jongho Lee

Purpose: To demonstrate the application of artificial-neural-network (ANN) for real-time processing of myelin water imaging (MWI).

Image and Video Processing

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