Exploring Label Efficiency with Semi-Supervision and Self-Supervision Methods
Software framework designed to leverage both Semi-Supervised and Self-Supervised Learning techniques to utilize unlabeled data effectively during the training process.
Overview
This project consists of a software framework designed to leverage both Semi-Supervised and Self-Supervised Learning techniques to utilize unlabeled data effectively during the training process. The framework is built to support a wide range of applications and tasks, providing detailed documentation for projects that aim to incorporate these advanced learning techniques.
Features
- Semi-Supervised Learning: The framework supports Semi-Supervised Learning techniques, allowing the model to leverage both labeled and unlabeled data during the training process.
- Self-Supervised Learning: The framework provides support for Self-Supervised Learning methods, enabling the model to learn from the data itself without requiring manual annotations.
- Modular Design: The framework is designed with a modular architecture, allowing users to easily integrate new models, datasets, and training strategies.
- Documentation: The framework includes detailed documentation and examples to guide users in implementing Semi-Supervised and Self-Supervised Learning techniques in their projects.
Technologies
- Programming Languages: Python
- Libraries: PyTorch