Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that SenseMag significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i. e., 7 types by SenseMag versus 4 types by the existing work in comparisons).
To learn with noisy clients, we propose a simple yet effective FL framework, named Federated Noisy Client Learning (Fed-NCL), which is a plug-and-play algorithm and contains two main components: a data quality measurement (DQM) to dynamically quantify the data quality of each participating client, and a noise robust aggregation (NRA) to adaptively aggregate the local models of each client by jointly considering the amount of local training data and the data quality of each client.
Inefficient traffic control may cause numerous problems such as traffic congestion and energy waste.
To better exploit the value of both pre-trained weights and unlabeled target examples, we introduce adaptive consistency regularization that consists of two complementary components: Adaptive Knowledge Consistency (AKC) on the examples between the source and target model, and Adaptive Representation Consistency (ARC) on the target model between labeled and unlabeled examples.
Both data and models are shared by robots to the cloud after semantic computing and training locally.
Furthermore, we developed federated learning open-source software based on FedCM.
We propose an integrated approach to active exploration by exploiting the Cartographer method as the base SLAM module for submap creation and performing efficient frontier detection in the geometrically co-aligned submaps induced by graph optimization.
While the existing multitask learning algorithms need to run backpropagation over both the source and target datasets and usually consume a higher gradient complexity, XMixup transfers the knowledge from source to target tasks more efficiently: for every class of the target task, XMixup selects the auxiliary samples from the source dataset and augments training samples via the simple mixup strategy.
RIFLE brings meaningful updates to the weights of deep CNN layers and improves low-level feature learning, while the effects of randomization can be easily converged throughout the overall learning procedure.
Recently, various novel deep learning techniques have been developed to process graph data, called graph neural networks (GNNs).
However, it is well known that an individual stock price is correlated with prices of other stocks in complex ways.
Deep convolutional neural networks are now widely deployed in vision applications, but a limited size of training data can restrict their task performance.
Compared with transfer learning and meta-learning, FIL is more suitable to be deployed in cloud robotic systems.
In this paper, a global descriptor for a LiDAR point cloud, called LiDAR Iris, is proposed for fast and accurate loop-closure detection.
Different from the conventional visual localization system, we design a novel visual optimization model by matching planar information between the LiDAR map and visual image.
Furthermore, we show how Tomato produces implementations of networks with various sizes running on single or multiple FPGAs.
The experimental results demonstrate that FIL is capable of increasing imitation learning of local robots in cloud robotic systems.
In ResNet-50, we achieved a 18. 08x CR with only 0. 24% loss in top-5 accuracy, outperforming existing compression methods.
Convolutional Neural Networks (CNNs) are widely used to solve classification tasks in computer vision.
Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources.
Nowadays, metro systems play an important role in meeting the urban transportation demand in large cities.