Multi-agent cooperative strategy learning method based on transfer Learning

Huang, Beinuo; Chen, Xin; Sun, Yipu; He, Wenpeng

doi:10.23919/ascc56756.2022.9828357

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High Level View Of Transfer Learning Paradigm1

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2023

2024

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“…Machine learning models are not capable of generalizing beyond the circumstances encountered to increase their performance [30]. They were inspired by the human capacity to transfer knowledge, which has led them to focus during training [31], which hampers their ability to on transfer learning to resolve these issues.…”

Section: High Level View Of Transfer Learning Paradigmmentioning

confidence: 99%

Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on DOTA

Hashmi,

Kumar Dwivedi,

Kumar

2024

JAMRIS

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In this paper, we proposed comparative research on the classification of various objects in satellite images using some pre-trained models of CNN (VGG-16, Inception-V3, ResNet-50, EfficientNet-B7) and R-CNN. In this research work, we have used the DOTA dataset, which combines data from 14 classes. We have implemented above mentioned pre-trained models of CNN, and R-CNN to achieve optimal results for accuracy as well as productivity. To detect objects like ships, tennis courts, swimming pools, vehicles, and harbors from remotely accessed images. In this study, we have used a convolutional neural network (CNN) as the base model. The transfer learning mechanism is employed to speed up the results and for complex computations. We have discovered with the help of experimental analysis that R-CNN and Inception-V3 are performing best out of the five pre-trained models.

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Section: High Level View Of Transfer Learning Paradigmmentioning

confidence: 99%