Self-supervised Learning
A demonstration of Self Supervised ML
This project focused on implementing a self-supervised learning method using PyTorch. The network is trained on the CIFAR10 dataset to classify how each image has been rotated and is then fine-tuned on a supervised task. I also implemented Grad-CAM for ResNet18 from scratch and provided visualizations and compared with initial gradient-based attention method.
Key Features:
- Implemented self-supervised learning method.
- Trained network on CIFAR10 dataset with and without pre-training.
- Reported results on various network variations.
Tools and Technologies:
- Python
- PyTorch
Source Code
The complete source code for this project is available on GitHub.
Image Credit: https://www.cs.toronto.edu/~kriz/cifar.html