Search Results for author:
Found 7 papers, 1 papers with code
Bootstrapping Face Detection with Hard Negative Examples
Recently significant performance improvement in face detection was made possible by deeply trained convolutional networks.
A Matrix-in-matrix Neural Network for Image Super Resolution
In recent years, deep learning methods have achieved impressive results with higher peak signal-to-noise ratio in single image super-resolution (SISR) tasks by utilizing deeper layers.
Perceptual Image Super-Resolution with Progressive Adversarial Network
Progressive growing enhances image resolution gradually, thereby preserving precision of recovered image.
Large Scale Global Optimization Algorithms for IoT Networks: A Comparative Study
To the best of the authors knowledge, this is the first time that LGSO algorithms are applied to the optimal power allocation problem in IoT networks.
Deep Image: A precious image based deep learning method for online malware detection in IoT Environment
In this way, a criterion is introduced that is used together with accuracy and FPR criteria for malware analysis in IoT environment.
Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation
Second, to resist overfitting issues caused by few training samples, a hyper-class embedding is learned by clustering all category embeddings for initialization and aligned with category embedding of the new class for enhancement, where learned knowledge assists to learn new knowledge, thus alleviating performance dependence on training data scale.
Multi-modal Instance Refinement for Cross-domain Action Recognition
In the source domain, some training samples are of low-relevance to target domain due to the difference in viewpoints, action styles, etc.
