60 citations · 98 across the 8 of their papers we have counts for
12 papers
PPG-to-ECG Signal Translation for Continuous Atrial Fibrillation Detection via Attention-based Deep State-Space Modeling
Khuong Vo, Mostafa El-Khamy, Yoojin Choi
Photoplethysmography (PPG) is a cost-effective and non-invasive technique that utilizes optical methods to measure cardiac physiology. PPG has become increasingly popular in health…
Zero-Shot Learning of a Conditional Generative Adversarial Network for Data-Free Network Quantization
Yoojin Choi, Mostafa El-Khamy, Jungwon Lee
We propose a novel method for training a conditional generative adversarial network (CGAN) without the use of training data, called zero-shot learning of a CGAN (ZS-CGAN). Zero-sho…
Toward Sustainable Continual Learning: Detection and Knowledge Repurposing of Similar Tasks
Sijia Wang, Yoojin Choi, Junya Chen +2
Most existing works on continual learning (CL) focus on overcoming the catastrophic forgetting (CF) problem, with dynamic models and replay methods performing exceptionally well. H…
Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay
Yoojin Choi, Mostafa El-Khamy, Jungwon Lee
This paper proposes two novel knowledge transfer techniques for class-incremental learning (CIL). First, we propose data-free generative replay (DF-GR) to mitigate catastrophic for…
Data-Free Network Quantization With Adversarial Knowledge Distillation
Yoojin Choi, Jihwan Choi, Mostafa El-Khamy +1
Network quantization is an essential procedure in deep learning for development of efficient fixed-point inference models on mobile or edge platforms. However, as datasets grow lar…
Variable Rate Deep Image Compression With a Conditional Autoencoder
Yoojin Choi, Mostafa El-Khamy, Jungwon Lee
In this paper, we propose a novel variable-rate learned image compression framework with a conditional autoencoder. Previous learning-based image compression methods mostly require…
Learning with Succinct Common Representation Based on Wyner's Common Information
J. Jon Ryu, Yoojin Choi, Young-Han Kim +2
A new bimodal generative model is proposed for generating conditional and joint samples, accompanied with a training method with learning a succinct bottleneck representation. The…
Jointly Sparse Convolutional Neural Networks in Dual Spatial-Winograd Domains
Yoojin Choi, Mostafa El-Khamy, Jungwon Lee
We consider the optimization of deep convolutional neural networks (CNNs) such that they provide good performance while having reduced complexity if deployed on either conventional…
Learning Sparse Low-Precision Neural Networks With Learnable Regularization
Yoojin Choi, Mostafa El-Khamy, Jungwon Lee
We consider learning deep neural networks (DNNs) that consist of low-precision weights and activations for efficient inference of fixed-point operations. In training low-precision…
Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints
Yoojin Choi, Mostafa El-Khamy, Jungwon Lee
We consider the optimization of deep convolutional neural networks (CNNs) such that they provide good performance while having reduced complexity if deployed on either conventional…
Universal Deep Neural Network Compression
Yoojin Choi, Mostafa El-Khamy, Jungwon Lee
In this paper, we investigate lossy compression of deep neural networks (DNNs) by weight quantization and lossless source coding for memory-efficient deployment. Whereas the previo…
Towards the Limit of Network Quantization
Yoojin Choi, Mostafa El-Khamy, Jungwon Lee
Network quantization is one of network compression techniques to reduce the redundancy of deep neural networks. It reduces the number of distinct network parameter values by quanti…