Continual Learning Challenge - CVPR 2024
Published:
🧠📊 Continual Learning Challenge - CLVISION 2024 - 5th CLVISION CVPR Workshop
Challenge Participation
We (in collaboration with Efstathios Karypidis) participated in the Continual Learning Challenge 2024, aiming at pushing the boundaries of continual learning techniques, addressing catastrophic forgetting, and improving model adaptation on sequential vision tasks.
💡 Challenge Overview
It is fair to assume that data is not cheap to acquire, store and label in real-world machine learning applications. Therefore, it is crucial to develop strategies that are flexible enough to learn from streams of experiences, without forgetting what has been learned previously. Additionally, contextual unlabelled data can also be exploited to integrate additional information into the model.
🎯 Challenge Highlights
Challenge focuses on classification task
- Methodologies Explored
- Developed and modified state-of-the-art continual learning algorithms
- Tackled issues like catastrophic forgetting
- Utilized unsupervised techniques, such as pseudo-labeling to harness unlabelled data
- Focus on Vision Tasks
- Applied continual learning techniques to image classification and segmentation tasks
- Leveraged recent advances in neural network architectures to improve performance
🚀 Impact & Learnings
- Enhanced understanding of continual learning challenges in practical scenarios
- Gained hands-on experience with the latest methods and metrics in lifelong learning
- Contributed to open-source frameworks and challenge leaderboards