Vinit Hegiste

According to our database, Vinit Hegiste authored at least 10 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturing.

Vinit Hegiste, Tatjana Legler, Martin Ruskowski

CoRR, 2024

Addressing Heterogeneity in Federated Learning: Challenges and Solutions for a Shared Production Environment.

Tatjana Legler, Vinit Hegiste, Ahmed Anwar, Martin Ruskowski

CoRR, 2024

Seamless Integration: Sampling Strategies in Federated Learning Systems.

Tatjana Legler, Vinit Hegiste, Martin Ruskowski

CoRR, 2024

Enhancing Object Detection with Hybrid dataset in Manufacturing Environments: Comparing Federated Learning to Conventional Techniques.

Vinit Hegiste, Snehal Walunj, Jibinraj Antony, Tatjana Legler, Martin Ruskowski

CoRR, 2024

FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data.

Ahmed Anwar, Brian B. Moser, Dayananda Herurkar, Federico Raue, Vinit Hegiste, Tatjana Legler, Andreas Dengel

CoRR, 2024

Enhancing Object Detection Performance for Small Objects through Synthetic Data Generation and Proportional Class-Balancing Technique: A Comparative Study in Industrial Scenarios.

Jibinraj Antony, Vinit Hegiste, Ali Nazeri, Hooman Tavakoli, Snehal Walunj, Christiane Plociennik, Martin Ruskowski

CoRR, 2024

2023

Federated Object Detection for Quality Inspection in Shared Production.

Vinit Hegiste, Tatjana Legler, Martin Ruskowski

CoRR, 2023

Federated Ensemble YOLOv5 - A Better Generalized Object Detection Algorithm.

Vinit Hegiste, Tatjana Legler, Martin Ruskowski

Proceedings of the Eighth International Conference on Fog and Mobile Edge Computing, 2023

Federated Object Detection for Quality Inspection in Shared Production.

Vinit Hegiste, Tatjana Legler, Kirill Fridman, Martin Ruskowski

Proceedings of the Eighth International Conference on Fog and Mobile Edge Computing, 2023

2022

Application of federated learning in manufacturing.

Vinit Hegiste, Tatjana Legler, Martin Ruskowski

CoRR, 2022