Summary of Semantically-shifted Incremental Adapter-tuning Is a Continual Vitransformer, by Yuwen Tan et al.
Semantically-Shifted Incremental Adapter-Tuning is A Continual ViTransformer
by Yuwen Tan, Qinhao Zhou, Xiang Xiang, Ke Wang, Yuchuan Wu, Yongbin Li
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A machine learning framework is proposed to overcome catastrophic forgetting in class-incremental learning (CIL), enabling models to continuously learn new classes without losing knowledge from previous ones. The framework uses adapter tuning and feature sampling techniques to improve the learning capacity of pre-trained models, eliminating the need for model expansion or storing past samples. Experimental results on five CIL benchmarks demonstrate the effectiveness of the approach, achieving state-of-the-art (SOTA) performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make machines learn and remember new things without forgetting what they already know. The goal is to help models improve by learning from more classes without losing their old knowledge. They do this using special techniques that help the model understand new information better, so it doesn’t need to store old images or grow bigger. This helps the machine learn faster and better, achieving a high level of performance. |
Keywords
» Artificial intelligence » Machine learning
