ABSTRACT
Curriculum learning (CL) is a learning paradigm that trains machine learning models the same way that humans learn, by starting with easier problems and gradually increasing the difficulty. This strategy has shown to improve generalization and convergence rate for a variety of models in computer vision and other fields. This workshop will go over the steps to implement CL using PyTorch. Both pre-defined and self-paced methods will be covered.
The outline of the workshop is as follows:
1. Overview of CL architecture
2. Implementing pre-defined CL
3. Implementing self-paced learning
4. Comparing CL performance with standard methods
5. Discussing results
Prerequisite knowledge
Attendees should be knowledgeable about these topics:
- Basic understanding of machine learning concepts
- Have an understanding of CNN architectures
- Has experience working with PyTorch
Dependencies
Dependencies needed for the Jupyter notebook:
- Python
- Pytorch (CUDA Version Recommended)
- Torchvision
ABOUT THE SPEAKER
Christian V. Tia is an undergraduate student in the BSMS Computer Science program at De La Salle University (DLSU). His research focuses on automated diabetic retinopathy classification and how it can be optimized in low-resource settings using smartphone-based fundus imaging.
