Search Results for author:
Found 13 papers, 3 papers with code
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
How to Manage Tiny Machine Learning at Scale: An Industrial Perspective
Tiny machine learning (TinyML) has gained widespread popularity where machine learning (ML) is democratized on ubiquitous microcontrollers, processing sensor data everywhere in real-time.
SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT
Therefore, this paper presents a framework called Semantic Low-Code Engineering for ML Applications (SeLoC-ML), built on a low-code platform to support the rapid development of ML applications in IIoT by leveraging Semantic Web technologies.
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution
We propose methodologies to train highly accurate and efficient deep convolutional neural networks (CNNs) for image super resolution (SR).
DN-ResNet: Efficient Deep Residual Network for Image Denoising
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.
Object Detection Using Generalization and Efficiency Balanced Co-Occurrence Features
In this paper, we propose a high-accuracy object detector based on co-occurrence features.
Basis Mapping Based Boosting for Object Detection
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.
Deep Robust Single Image Depth Estimation Neural Network Using Scene Understanding
We introduce two different scene understanding modules based on scene classification and coarse depth estimation respectively.
TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching
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.
TinyOL: TinyML with Online-Learning on Microcontrollers
The neural network is first trained using a large amount of pre-collected data on a powerful machine and then flashed to MCUs.
The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoT
Focusing on comprehensive networking, big data, and artificial intelligence, the Industrial Internet-of-Things (IIoT) facilitates efficiency and robustness in factory operations.
TinyReptile: TinyML with Federated Meta-Learning
Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs).
TinyMetaFed: Efficient Federated Meta-Learning for TinyML
The field of Tiny Machine Learning (TinyML) has made substantial advancements in democratizing machine learning on low-footprint devices, such as microcontrollers.
