Search Results for author: Yoseob Han

Found 11 papers, 4 papers with code

Distance Sampling-based Paraphraser Leveraging ChatGPT for Text Data Manipulation

no code implementations1 May 2024 Yoori Oh, Yoseob Han, Kyogu Lee

To overcome the limitation, we introduce a method that employs a distance sampling-based paraphraser leveraging ChatGPT, utilizing distance function to generate a controllable distribution of manipulated text data.

Text Augmentation Text Retrieval

Semi-supervised learning for continuous emotional intensity controllable speech synthesis with disentangled representations

no code implementations11 Nov 2022 Yoori Oh, Juheon Lee, Yoseob Han, Kyogu Lee

However, the emotional latent space generated from the existing models is difficult to control the continuous emotional intensity because of the entanglement of features like emotions, speakers, etc.

Emotional Speech Synthesis

Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal

no code implementations17 Jun 2019 Yoseob Han, Junyoung Kim, Jong Chul Ye

Conebeam CT using a circular trajectory is quite often used for various applications due to its relative simple geometry.

One Network to Solve All ROIs: Deep Learning CT for Any ROI using Differentiated Backprojection

1 code implementation1 Oct 2018 Yoseob Han, Jong Chul Ye

The first type learns ROI size-specific cupping artifacts from the analytic reconstruction images, whereas the second type network is to learn to invert the finite Hilbert transform from the truncated differentiated backprojection (DBP) data.

k-Space Deep Learning for Reference-free EPI Ghost Correction

no code implementations1 Jun 2018 Juyoung Lee, Yoseob Han, Jae-Kyun Ryu, Jang-Yeon Park, Jong Chul Ye

Reconstruction results using 3T and 7T in-vivo data showed that the proposed method outperformed the image quality compared to the existing methods, and the computing time is much faster. The proposed k-space deep learning for EPI ghost correction is highly robust and fast, and can be combined with acceleration, so that it can be used as a promising correction tool for high-field MRI without changing the current acquisition protocol.

Matrix Completion

k-Space Deep Learning for Accelerated MRI

1 code implementation10 May 2018 Yoseob Han, Leonard Sunwoo, Jong Chul Ye

The annihilating filter-based low-rank Hankel matrix approach (ALOHA) is one of the state-of-the-art compressed sensing approaches that directly interpolates the missing k-space data using low-rank Hankel matrix completion.

Denoising Matrix Completion

Deep Learning Reconstruction for 9-View Dual Energy CT Baggage Scanner

no code implementations4 Jan 2018 Yoseob Han, Jingu Kang, Jong Chul Ye

For homeland and transportation security applications, 2D X-ray explosive detection system (EDS) have been widely used, but they have limitations in recognizing 3D shape of the hidden objects.

3D Reconstruction Computed Tomography (CT)

Deep Learning Interior Tomography for Region-of-Interest Reconstruction

no code implementations29 Dec 2017 Yoseob Han, Jawook Gu, Jong Chul Ye

Interior tomography for the region-of-interest (ROI) imaging has advantages of using a small detector and reducing X-ray radiation dose.

Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT

3 code implementations28 Aug 2017 Yoseob Han, Jong Chul Ye

X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose.

Computed Tomography (CT)

Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems

4 code implementations3 Jul 2017 Jong Chul Ye, Yoseob Han, Eunju Cha

Using numerical experiments with various inverse problems, we demonstrated that our deep convolution framelets network shows consistent improvement over existing deep architectures. This discovery suggests that the success of deep learning is not from a magical power of a black-box, but rather comes from the power of a novel signal representation using non-local basis combined with data-driven local basis, which is indeed a natural extension of classical signal processing theory.

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