AI BriefingLG AI Research 238
Key point
This introduces the Continual Learning research trends based on Transformers and self-supervised learning presented at CVPR 2022.
Details
Artificial neural networks show excellent performance on specific tasks, but they face the problem of Catastrophic Forgetting, where they forget previous information when learning new tasks. Research on Continual Learning to solve this problem is actively underway.
The first major research direction is continual learning using Transformer models. DyTox (Dynamic Token Expansion), proposed to overcome the limitations of existing CNN-based models, leverages the characteristics of Transformers to efficiently learn new tasks while minimizing model expansion and without complex hyperparameter tuning. This has demonstrated excellent performance on various datasets such as ImageNet and CIFAR-100.
The second direction is Unsupervised (or Self-Supervised) Continual Learning. According to recent research, the Self-supervised Contrastive Learning approach, which learns without labels, has been proven to be more effective at reducing catastrophic forgetting than existing supervised learning approaches that use Cross Entropy.
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