Investigating a Baseline Of Self Supervised Learning Towards Reducing Labeling Costs For Image Classification

• URL: http://arxiv.org/abs/2108.07464v1
• Date: Tue, 17 Aug 2021 06:43:05 GMT
• Title: Investigating a Baseline Of Self Supervised Learning Towards Reducing Labeling Costs For Image Classification
• Authors: Hilal AlQuabeh, Ameera Bawazeer, Abdulateef Alhashmi
• Abstract summary: The study implements the kaggle.com' cats-vs-dogs dataset, Mnist and Fashion-Mnist to investigate the self-supervised learning task. Results show that the pretext process in the self-supervised learning improves the accuracy around 15% in the downstream classification task.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data labeling in supervised learning is considered an expensive and infeasible tool in some conditions. The self-supervised learning method is proposed to tackle the learning effectiveness with fewer labeled data, however, there is a lack of confidence in the size of labeled data needed to achieve adequate results. This study aims to draw a baseline on the proportion of the labeled data that models can appreciate to yield competent accuracy when compared to training with additional labels. The study implements the kaggle.com' cats-vs-dogs dataset, Mnist and Fashion-Mnist to investigate the self-supervised learning task by implementing random rotations augmentation on the original datasets. To reveal the true effectiveness of the pretext process in self-supervised learning, the original dataset is divided into smaller batches, and learning is repeated on each batch with and without the pretext pre-training. Results show that the pretext process in the self-supervised learning improves the accuracy around 15% in the downstream classification task when compared to the plain supervised learning.

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