Unsupervised Domain Adaptation for Image Classification and Object Detection Using Guided Transfer Learning Approach and JS Divergence

Unsupervised domain adaptation (UDA) is a transfer learning technique utilized in deep learning. UDA aims to reduce the distribution gap between labeled source and unlabeled target domains by adapting a model through fine-tuning. Typically, UDA approaches assume the same categories in both domains....

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Bibliographic Details
Main Authors:	Ganatra, A. (Author), Goel, P. (Author)
Format:	Article
Language:	English
Published:	MDPI 2023
Subjects:	Classification (of information) convolutional neural network Convolutional neural network Convolutional neural networks deep learning Deep learning domain adaptation Domain adaptation Feature extraction Fine tuning image classification Image classification Image objects Images classification Learning approach Learning systems object detection Object detection Object recognition Objects detection Target domain transfer learning Transfer learning
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Unsupervised Domain Adaptation for Image Classification and Object Detection Using Guided Transfer Learning Approach and JS Divergence

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