Investigating Contrastive Pair Learning’s Frontiers in Supervised, Semisupervised, and Self-Supervised Learning

• Published in: Journal of imaging Vol. 10; no. 8; p. 196
• Main Authors: Sabiri, Bihi, Khtira, Amal, El Asri, Bouchra, Rhanoui, Maryem
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13.08.2024; MDPI
• Subjects: Accuracy; Artificial intelligence; Classification; Computer Science; contrastive learning; contrastive loss; Datasets; Machine learning; Natural language processing; representation learning; Representations; Self-supervised learning; similarity metric; supervised learning; Unsupervised learning; contrastive learning; representation learning; supervised learning; contrastive loss; similarity metric; unsupervised learning; unsupervised pretraining
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging10080196

In recent years, contrastive learning has been a highly favored method for self-supervised representation learning, which significantly improves the unsupervised training of deep image models. Self-supervised learning is a subset of unsupervised learning in which the learning process is supervised by creating pseudolabels from the data themselves. Using supervised final adjustments after unsupervised pretraining is one way to take the most valuable information from a vast collection of unlabeled data and teach from a small number of labeled instances. This study aims firstly to compare contrastive learning with other traditional learning models; secondly to demonstrate by experimental studies the superiority of contrastive learning during classification; thirdly to fine-tune performance using pretrained models and appropriate hyperparameter selection; and finally to address the challenge of using contrastive learning techniques to produce data representations with semantic meaning that are independent of irrelevant factors like position, lighting, and background. Relying on contrastive techniques, the model efficiently captures meaningful representations by discerning similarities and differences between modified copies of the same image. The proposed strategy, involving unsupervised pretraining followed by supervised fine-tuning, improves the robustness, accuracy, and knowledge extraction of deep image models. The results show that even with a modest 5% of data labeled, the semisupervised model achieves an accuracy of 57.72%. However, the use of supervised learning with a contrastive approach and careful hyperparameter tuning increases accuracy to 85.43%. Further adjustment of the hyperparameters resulted in an excellent accuracy of 88.70%.