1 code implementation • 2 Feb 2023 • Hui-Po Wang, Dingfan Chen, Raouf Kerkouche, Mario Fritz
This work proposes FedLAP-DP, a novel privacy-preserving approach for federated learning.
1 code implementation • 11 Oct 2021 • Hui-Po Wang, Sebastian U. Stich, Yang He, Mario Fritz
Federated learning is a powerful distributed learning scheme that allows numerous edge devices to collaboratively train a model without sharing their data.
1 code implementation • CVPR 2021 • Yan-Cheng Huang, Yi-Hsin Chen, Cheng-You Lu, Hui-Po Wang, Wen-Hsiao Peng, Ching-Chun Huang
Our Long Short-Term Memory Video Rescaling Network (LSTM-VRN) leverages temporal information in the low-resolution video to form an explicit prediction of the missing high-frequency information for upscaling.
no code implementations • 15 Dec 2020 • Yang He, Hui-Po Wang, Maximilian Zenk, Mario Fritz
Despite notable progress in gradient compression, the existing quantization methods require further improvement when low-bits compression is applied, especially the overall systems often degenerate a lot when quantization are applied in double directions to compress model weights and gradients.
1 code implementation • CVPR 2021 • Hui-Po Wang, Ning Yu, Mario Fritz
While Generative Adversarial Networks (GANs) show increasing performance and the level of realism is becoming indistinguishable from natural images, this also comes with high demands on data and computation.
no code implementations • 20 May 2020 • Hui-Po Wang, Tribhuvanesh Orekondy, Mario Fritz
Personal photos of individuals when shared online, apart from exhibiting a myriad of memorable details, also reveals a wide range of private information and potentially entails privacy risks (e. g., online harassment, tracking).
no code implementations • ICLR 2018 • Hui-Po Wang, Wen-Hsiao Peng, Wei-Jan Ko
Most deep latent factor models choose simple priors for simplicity, tractability or not knowing what prior to use.
1 code implementation • CVPR 2019 • Wei-Lun Chang, Hui-Po Wang, Wen-Hsiao Peng, Wei-Chen Chiu
In this paper we tackle the problem of unsupervised domain adaptation for the task of semantic segmentation, where we attempt to transfer the knowledge learned upon synthetic datasets with ground-truth labels to real-world images without any annotation.
Ranked #25 on Image-to-Image Translation on SYNTHIA-to-Cityscapes