2 citations · 6 across the 16 of their papers we have counts for
16 papers
Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels
Sihan Wang, Fuping Wu, Lei Li +3
Cardiac segmentation is in great demand for clinical practice. Due to the enormous labor of manual delineation, unsupervised segmentation is desired. The ill-posed optimization pro…
Monitored Distillation for Positive Congruent Depth Completion
Tian Yu Liu, Parth Agrawal, Allison Chen +2
We propose a method to infer a dense depth map from a single image, its calibration, and the associated sparse point cloud. In order to leverage existing models (teachers) that pro…
Small Lesion Segmentation in Brain MRIs with Subpixel Embedding
Alex Wong, Allison Chen, Yangchao Wu +4
We present a method to segment MRI scans of the human brain into ischemic stroke lesion and normal tissues. We propose a neural network architecture in the form of a standard encod…
An Adaptive Framework for Learning Unsupervised Depth Completion
Alex Wong, Xiaohan Fei, Byung-Woo Hong +1
We present a method to infer a dense depth map from a color image and associated sparse depth measurements. Our main contribution lies in the design of an annealing process for det…
Generative Adversarial Networks via a Composite Annealing of Noise and Diffusion
Kensuke Nakamura, Simon Korman, Byung-Woo Hong
Generative adversarial network (GAN) is a framework for generating fake data using a set of real examples. However, GAN is unstable in the training stage. In order to stabilize GAN…
Regularization in network optimization via trimmed stochastic gradient descent with noisy label
Kensuke Nakamura, Bong-Soo Sohn, Kyoung-Jae Won +1
Regularization is essential for avoiding over-fitting to training data in network optimization, leading to better generalization of the trained networks. The label noise provides a…
Stochastic batch size for adaptive regularization in deep network optimization
Kensuke Nakamura, Stefano Soatto, Byung-Woo Hong
We propose a first-order stochastic optimization algorithm incorporating adaptive regularization applicable to machine learning problems in deep learning framework. The adaptive re…
Adaptive Regularization via Residual Smoothing in Deep Learning Optimization
Junghee Cho, Junseok Kwon, Byung-Woo Hong
We present an adaptive regularization algorithm that can be effectively applied to the optimization problem in deep learning framework. Our regularization algorithm aims to take in…
Adaptive Weight Decay for Deep Neural Networks
Kensuke Nakamura, Byung-Woo Hong
Regularization in the optimization of deep neural networks is often critical to avoid undesirable over-fitting leading to better generalization of model. One of the most popular re…
Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction
Alex Wong, Byung-Woo Hong, Stefano Soatto
Supervised learning methods to infer (hypothesize) depth of a scene from a single image require costly per-pixel ground-truth. We follow a geometric approach that exploits abundant…
Block-Cyclic Stochastic Coordinate Descent for Deep Neural Networks
Kensuke Nakamura, Stefano Soatto, Byung-Woo Hong
We present a stochastic first-order optimization algorithm, named BCSC, that adds a cyclic constraint to stochastic block-coordinate descent. It uses different subsets of the data…
Adaptive Regularization of Some Inverse Problems in Image Analysis
Byung-Woo Hong, Ja-Keoung Koo, Martin Burger +1
We present an adaptive regularization scheme for optimizing composite energy functionals arising in image analysis problems. The scheme automatically trades off data fidelity and r…
Multi-Label Segmentation via Residual-Driven Adaptive Regularization
Byung-Woo Hong, Ja-Keoung Koo, Stefano Soatto
We present a variational multi-label segmentation algorithm based on a robust Huber loss for both the data and the regularizer, minimized within a convex optimization framework. We…
Adaptive Regularization in Convex Composite Optimization for Variational Imaging Problems
Byung-Woo Hong, Ja-Keoung Koo, Hendrik Dirks +1
We propose an adaptive regularization scheme in a variational framework where a convex composite energy functional is optimized. We consider a number of imaging problems including…
Coarse-to-Fine Segmentation With Shape-Tailored Scale Spaces
Ganesh Sundaramoorthi, Naeemullah Khan, Byung-Woo Hong
We formulate a general energy and method for segmentation that is designed to have preference for segmenting the coarse structure over the fine structure of the data, without smoot…
Tracking via Motion Estimation with Physically Motivated Inter-Region Constraints
Omar Arif, Ganesh Sundaramoorthi, Byung-Woo Hong +1
In this paper, we propose a method for tracking structures (e.g., ventricles and myocardium) in cardiac images (e.g., magnetic resonance) by propagating forward in time a previous…