Learn new objects without forgetting old ones using stable diffusion to generate images with SDDGR
Learn new objects without forgetting old ones using stable diffusion to generate images with SDDGR
SDDGR: Stable Diffusion-based Deep Generative Replay for Class Incremental Object Detection
arXiv paper abstract https://arxiv.org/abs/2402.17323
arXiv PDF paper https://arxiv.org/pdf/2402.17323.pdf
In ... class incremental learning (CIL), generative replay has become ... a method to mitigate the catastrophic forgetting, alongside the continuous improvements in generative models.
... propose a novel approach called stable diffusion deep generative replay (SDDGR) for CIOD.
... method utilizes a diffusion-based generative model with pre-trained text-to-diffusion networks to generate realistic and diverse synthetic images.
SDDGR incorporates an iterative refinement strategy to produce high-quality images encompassing old classes ... adopt an L2 knowledge distillation technique to improve the retention of prior knowledge in synthetic images.
... approach includes pseudo-labeling for old objects within new task images, preventing misclassification as background elements.
... SDDGR significantly outperforms existing algorithms, achieving a new state-of-the-art in various CIOD scenarios ...
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