Adapted SimCLR framework on modified ResNet, comparing with RotNet and exploring semi-supervised fine-tuning.

• SimCLR

• RotNet

• Yujie Huang

• Yi Li

• Haobo Yuan

Exploration and implementation of two self-supervised frameworks, SimCLR and RotNet, with ResNet-20 on CIFAR-10, underscoring the potency of self-supervised learning in scenarios where labeled data is scarce.

Discussion on the dependency on labeled datasets in machine learning and how self-supervised learning approaches like SimCLR are addressing this challenge.

Review of seminal frameworks in self-supervised learning, focusing on contrastive learning, and its implications for future research directions.

Detailed exposition of the methodology encompassing data augmentation strategies, implementation specifics of SimCLR with ResNet-20, and the contrastive loss function.

Outline of the experimental setup, description of CIFAR-10, implementation details of SimCLR, and semi-supervised learning performance metrics.

Validation of the self-supervised learning approach, its advantages in semi-supervised settings, and its potential in enhancing model resilience and generalization.