מושגי ליבה
Using synthetic images of future classes generated by pre-trained text-to-image diffusion models can significantly improve the performance of exemplar-free class incremental learning methods relying on a frozen feature extractor.
תקציר
The content discusses a novel approach called Future-Proof Class Incremental Learning (FPCIL) to address the limitations of exemplar-free class incremental learning (EFCIL) methods that rely on a frozen feature extractor.
Key highlights:
- EFCIL methods are highly dependent on the data used to train the feature extractor during the initial step, and may struggle when the number of classes is limited.
- FPCIL leverages pre-trained text-to-image diffusion models to generate synthetic images of future classes and uses them jointly with the current dataset to train the feature extractor during the initial step.
- Experiments on CIFAR100 and ImageNet-Subset show that FPCIL can significantly improve the performance of state-of-the-art EFCIL methods, especially in the most challenging settings where the initial step contains few classes.
- Using synthetic images of future classes achieves higher performance than using real images from different classes, demonstrating the benefits of this approach.
סטטיסטיקה
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