Summary of Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning, by Mark D. Mcdonnell et al.
Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning
by Mark D. McDonnell, Dong Gong, Ehsan Abbasnejad, Anton van den Hengel
First submitted to arxiv on: 12 Mar 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 In this paper, researchers propose a novel approach to continual learning by using a combination of large language and image generation models to predict how a dataset might evolve over time. The authors generate text descriptions of potential classes that may appear in the future data stream using a large language model, which are then used to create synthetic labelled image samples through Stable Diffusion. These synthetic images are employed for supervised pre-training, but the classification head is discarded before beginning continual learning. The authors find that pre-training models in this manner improves multiple Class Incremental Learning (CIL) methods on fine-grained image classification benchmarks. By leveraging this approach, the paper demonstrates a valuable input to existing CIL methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn better by using words and pictures to predict what new things they might see in the future. The researchers use two special models to generate text and images that are similar to real ones, but not exactly the same. They then use these fake data points to teach a computer how to learn and adapt over time. This approach helps machines get better at recognizing objects in pictures, even when those objects have never been seen before. |
Keywords
* Artificial intelligence * Classification * Continual learning * Diffusion * Image classification * Image generation * Large language model * Supervised
