80 citations · 551 across the 42 of their papers we have counts for
47 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…
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…
Towards Fair Federated Learning with Zero-Shot Data Augmentation
Weituo Hao, Mostafa El-Khamy, Jungwon Lee +4
Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models while having no access to the…
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…
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
Abdelrahman Abdelhamed, Mahmoud Afifi, Radu Timofte +87
This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new versi…
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr, Martin Danelljan, Radu Timofte +43
This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world settin…