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- Chopra, Muskaan, et al.
(författare)
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Domain Adaptable Self-supervised Representation Learning on Remote Sensing Satellite Imagery
- 2023
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Ingår i: IJCNN 2023 - International Joint Conference on Neural Networks, Conference Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665488686 - 9781665488679
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Konferensbidrag (refereegranskat)abstract
- This work presents a novel domain adaption paradigm for studying contrastive self-supervised representation learning and knowledge transfer using remote sensing satellite data. Major state-of-the-art remote sensing visual domain ef-forts primarily focus on fully supervised learning approaches that rely entirely on human annotations. On the other hand, human annotations in remote sensing satellite imagery are always subject to limited quantity due to high costs and domain expertise, making transfer learning a viable alternative. The proposed approach investigates the knowledge transfer of self-supervised representations across the distinct source and target data distributions in depth in the remote sensing data domain. In this arrangement, self-supervised contrastive learning- based pretraining is performed on the source dataset, and downstream tasks are performed on the target datasets in a round-robin fashion. Experiments are conducted on three publicly avail-able datasets, UC Merced Landuse (UCMD), SIRI-WHU, and MLRSNet, for different downstream classification tasks versus label efficiency. In self-supervised knowledge transfer, the pro-posed approach achieves state-of-the-art performance with label efficiency labels and outperforms a fully supervised setting. A more in-depth qualitative examination reveals consistent evidence for explainable representation learning. The source code and trained models are published on GitHub1.
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| 3. |
- Gupta, Ekta, et al.
(författare)
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Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus Images
- 2023
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Ingår i: IJCNN 2023 - International Joint Conference on Neural Networks, Conference Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665488686 - 9781665488679
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Konferensbidrag (refereegranskat)abstract
- This work presents a novel label-efficient self-supervised representation learning-based approach for classifying diabetic retinopathy (DR) images in cross-domain settings. Most of the existing DR image classification methods are based on supervised learning which requires a lot of time-consuming and expensive medical domain experts-annotated data for training. The proposed approach uses the prior learning from the source DR image dataset to classify images drawn from the target datasets. The image representations learned from the unlabeled source domain dataset through contrastive learning are used to classify DR images from the target domain dataset. Moreover, the proposed approach requires a few labeled images to perform successfully on DR image classification tasks in cross-domain settings. The proposed work experiments with four publicly available datasets: EyePACS, APTOS 2019, MESSIDOR-I, and Fundus Images for self-supervised representation learning-based DR image classification in cross-domain settings. The proposed method achieves state-of-the-art results on binary and multi-classification of DR images, even in cross-domain settings. The proposed method outperforms the existing DR image binary and multi-class classification methods proposed in the literature. The proposed method is also validated qualitatively using class activation maps, revealing that the method can learn explainable image representations. The source code and trained models are published on GitHub11https://github.com/prakashchhipa/Learning-Self-Supervised-Representations-for-Label-Efficient-Cross-Domain-Knowledge-Transfer-on-DRF.
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