Summary of Anytime Continual Learning For Open Vocabulary Classification, by Zhen Zhu et al.
Anytime Continual Learning for Open Vocabulary Classification
by Zhen Zhu, Yiming Gong, Derek Hoiem
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The proposed approach for anytime continual learning (AnytimeCL) in open vocabulary image classification enables a system to predict any set of labels at any time and efficiently update when receiving new training samples. This dynamic model improves over recent methods by integrating partially fine-tuned models with fixed open vocabulary models, allowing for continual improvement. Additionally, attention-weighted PCA compression reduces storage and computation while maintaining accuracy. The approach is validated through experiments that test flexibility in learning and inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AnytimeCL is a new way to learn from images. Imagine you’re trying to identify different types of animals or objects. Normally, you would need to train the system on all the labels at once. But AnytimeCL lets the system learn just one label at a time, even if it’s not the same as before. It does this by using two models that work together: one that can already recognize some things and another that is more flexible. This helps the system get better over time without needing to relearn everything. The team also found a way to make the system use less storage and computation without sacrificing accuracy. |
Keywords
» Artificial intelligence » Attention » Continual learning » Image classification » Inference » Pca
