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Found 7 papers, 0 papers with code
Enhancing Object Detection Performance for Small Objects through Synthetic Data Generation and Proportional Class-Balancing Technique: A Comparative Study in Industrial Scenarios
Object Detection (OD) has proven to be a significant computer vision method in extracting localized class information and has multiple applications in the industry.
State Derivative Normalization for Continuous-Time Deep Neural Networks
However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods.
Federated K-Means Clustering via Dual Decomposition-based Distributed Optimization
By splitting the training process across multiple nodes its efficiency can be significantly increased.
Federated Ensemble YOLOv5 -- A Better Generalized Object Detection Algorithm
Federated learning (FL) has gained significant traction as a privacy-preserving algorithm, but the underlying resemblances of federated learning algorithms like Federated averaging (FedAvg) or Federated SGD (Fed SGD) to ensemble learning algorithms have not been fully explored.
Federated Object Detection for Quality Inspection in Shared Production
Federated learning (FL) has emerged as a promising approach for training machine learning models on decentralized data without compromising data privacy.
Application of federated learning in manufacturing
A vast amount of data is created every minute, both in the private sector and industry.
Small Object Detection for Near Real-Time Egocentric Perception in a Manual Assembly Scenario
We describe a near real-time small object detection pipeline for egocentric perception in a manual assembly scenario: We generate a training data set based on CAD data and realistic backgrounds in Unity.
