2 citations · 3 across the 3 of their papers we have counts for
9 papers
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…
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…