activity
20182021
most citedDrivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework

125 citations · 133 across the 5 of their papers we have counts for

collaborators

7 papers

cs.LG20214 cited

Information-Theoretic Analysis of Epistemic Uncertainty in Bayesian Meta-learning

Sharu Theresa Jose, Sangwoo Park, Osvaldo Simeone

The overall predictive uncertainty of a trained predictor can be decomposed into separate contributions due to epistemic and aleatoric uncertainty. Under a Bayesian formulation, as…

cs.IT20212 cited

Meta-ViterbiNet: Online Meta-Learned Viterbi Equalization for Non-Stationary Channels

Tomer Raviv, Sangwoo Park, Nir Shlezinger +3

Deep neural networks (DNNs) based digital receivers can potentially operate in complex environments. However, the dynamic nature of communication channels implies that in some scen…

cs.CV20202 cited

Balance-Oriented Focal Loss with Linear Scheduling for Anchor Free Object Detection

Hopyong Gil, Sangwoo Park, Yusang Park +3

Most existing object detectors suffer from class imbalance problems that hinder balanced performance. In particular, anchor free object detectors have to solve the background imbal…

cs.CV2019125 cited

Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework

Jongmin Yu, Sangwoo Park, Sangwook Lee +1

We propose a condition-adaptive representation learning framework for the driver drowsiness detection based on 3D-deep convolutional neural network. The proposed framework consists…

eess.AS2019

Correlation Distance Skip Connection Denoising Autoencoder (CDSK-DAE) for Speech Feature Enhancement

Alzahra Badi, Sangwook Park, David K. Han +1

Performance of learning based Automatic Speech Recognition (ASR) is susceptible to noise, especially when it is introduced in the testing data while not presented in the training d…

cs.SD2019

Sinusoidal wave generating network based on adversarial learning and its application: synthesizing frog sounds for data augmentation

Sangwook Park, David K. Han, Hanseok Ko

Simulators that generate observations based on theoretical models can be important tools for development, prediction, and assessment of signal processing algorithms. In order to de…