Summary of Forget but Recall: Incremental Latent Rectification in Continual Learning, by Nghia D. Nguyen et al.
Forget but Recall: Incremental Latent Rectification in Continual Learning
by Nghia D. Nguyen, Hieu Trung Nguyen, Ang Li, Hoang Pham, Viet Anh Nguyen, Khoa D. Doan
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates an unexplored Continual Learning (CL) approach called Incremental Latent Rectification (ILR), which aims to mitigate catastrophic forgetting in deep neural networks. ILR learns to propagate corrections from the current trained DNN back to the representation space of old tasks, allowing for easier predictive decisions. This is achieved through a chain of small representation mapping networks, or rectifier units. The paper evaluates ILR on several benchmarks, including CIFAR10, CIFAR100, and Tiny ImageNet, demonstrating its effectiveness compared to existing CL methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps deep neural networks learn new things without forgetting what they already know. Right now, these networks have a problem where they forget old information when learning new things. To fix this, researchers are trying different approaches to help the network remember past knowledge. This paper looks at a new way called Incremental Latent Rectification (ILR). ILR helps the network learn by correcting its mistakes and remembering what it already knows. The researchers tested ILR on several challenges and found that it works well. |
Keywords
» Artificial intelligence » Continual learning
