Ablation study of self-supervised learning for image classification

• URL: http://arxiv.org/abs/2112.02297v1
• Date: Sat, 4 Dec 2021 09:59:01 GMT
• Title: Ablation study of self-supervised learning for image classification
• Authors: Ilias Papastratis
• Abstract summary: This project focuses on the self-supervised training of convolutional neural networks (CNNs) and transformer networks for the task of image recognition. A simple siamese network with different backbones is used in order to maximize the similarity of two augmented transformed images from the same source image.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This project focuses on the self-supervised training of convolutional neural networks (CNNs) and transformer networks for the task of image recognition. A simple siamese network with different backbones is used in order to maximize the similarity of two augmented transformed images from the same source image. In this way, the backbone is able to learn visual information without supervision. Finally, the method is evaluated on three image recognition datasets.

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