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Learning to Learn: How to Continuously Teach Humans and Machines
Parantak Singh1,2, You Li2,3, Ankur Sikarwar1,2
1Nanyang Technological University (NTU), Singapore.
... IEEE International Conference on Computer Vision Workshops. IEEE International Conference on Computer Vision
|May 24, 2024
Summary
Optimizing the learning sequence, or curriculum, significantly improves knowledge transfer for both humans and machines in continual learning settings. Effective curricula for humans show strong correlation with those beneficial for machine learning algorithms.
Area of Science:
- Artificial Intelligence
- Cognitive Science
- Machine Learning
Background:
- Curriculum design is crucial for effective knowledge transfer in education.
- Prior research on curriculum design primarily focused on single, offline tasks.
- The online class-incremental continual learning setting involves learning tasks sequentially.
Purpose of the Study:
- To investigate the impact of task order on learning outcomes in online class-incremental continual learning.
- To compare the effectiveness of curricula for humans and machine learning algorithms.
- To develop an automated algorithm for designing effective curricula.
Main Methods:
- Studied the effect of task sequencing in online class-incremental continual learning.
- Introduced a novel object recognition dataset for human experiments.
- Developed and evaluated a Curriculum Designer (CD) algorithm based on inter-class feature similarities.
Main Results:
- Curriculum order significantly influences learning outcomes for both humans and machine learning algorithms.
- Effective curricula for humans are highly correlated with those effective for machines.
- The proposed CD algorithm shows significant overlap with empirically effective curricula.
Conclusions:
- Optimized curricula enhance knowledge transfer and minimize forgetting in continual learning.
- A unified framework for studying and designing curricula for humans and machines is established.
- Automated curriculum design for online continual learning is a promising research direction.
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