Search Results for author: Haoyu Ren

Found 10 papers, 2 papers with code

How to Manage Tiny Machine Learning at Scale: An Industrial Perspective

1 code implementation18 Feb 2022 Haoyu Ren, Darko Anicic, Thomas Runkler

Tiny machine learning (TinyML) has gained widespread popularity where machine learning (ML) is democratized on ubiquitous microcontrollers, processing sensor data everywhere in real-time.

The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoT

no code implementations4 May 2021 Haoyu Ren, Darko Anicic, Thomas Runkler

Focusing on comprehensive networking, big data, and artificial intelligence, the Industrial Internet-of-Things (IIoT) facilitates efficiency and robustness in factory operations.

TinyOL: TinyML with Online-Learning on Microcontrollers

no code implementations15 Mar 2021 Haoyu Ren, Darko Anicic, Thomas Runkler

The neural network is first trained using a large amount of pre-collected data on a powerful machine and then flashed to MCUs.

online learning

TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching

no code implementations11 Jun 2019 Mostafa El-Khamy, Haoyu Ren, Xianzhi Du, Jungwon Lee

In this paper, we introduce the problem of estimating the real world depth of elements in a scene captured by two cameras with different field of views, where the first field of view (FOV) is a Wide FOV (WFOV) captured by a wide angle lens, and the second FOV is contained in the first FOV and is captured by a tele zoom lens.

Depth Estimation Disparity Estimation +2

Deep Robust Single Image Depth Estimation Neural Network Using Scene Understanding

no code implementations7 Jun 2019 Haoyu Ren, Mostafa El-Khamy, Jungwon Lee

We introduce two different scene understanding modules based on scene classification and coarse depth estimation respectively.

Depth Estimation General Classification +2

DN-ResNet: Efficient Deep Residual Network for Image Denoising

no code implementations16 Oct 2018 Haoyu Ren, Mostafa El-Khamy, Jungwon Lee

The results show that DN-ResNets are more efficient, robust, and perform better denoising than current state of art deep learning methods, as well as the popular variants of the BM3D algorithm, in cases of blind and non-blind denoising of images corrupted with Poisson, Gaussian or Poisson-Gaussian noise.

Image Denoising Image Enhancement +1

CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution

no code implementations11 Nov 2017 Haoyu Ren, Mostafa El-Khamy, Jungwon Lee

We propose methodologies to train highly accurate and efficient deep convolutional neural networks (CNNs) for image super resolution (SR).

Image Super-Resolution

Basis Mapping Based Boosting for Object Detection

no code implementations CVPR 2015 Haoyu Ren, Ze-Nian Li

We show that the basis mapping based weak classifier is an approximation of kernel weak classifiers while keeping the same computation cost as linear weak classifiers.

Object Detection Pedestrian Detection

Cannot find the paper you are looking for? You can Submit a new open access paper.