Pretraining Image Backbones with Deep Generative Models - arXiv
You can access the full paper through the following sources: OpenAccess (TheCVF) arXiv Preprint IEEE Xplore
The primary scientific paper related to is titled "DreamTeacher: Pretraining Image Backbones with Deep Generative Models" , published at ICCV 2023 . DreamTeacher-Ep1Pt1.2-pc_[juegosXXXgratis.com].zip
[2307.07487] DreamTeacher: Pretraining Image Backbones with Deep Generative Models.
: DreamTeacher significantly outperforms existing self-supervised learning approaches on benchmarks like ImageNet , ADE20K (semantic segmentation), and MSCOCO (instance segmentation). Pretraining Image Backbones with Deep Generative Models -
: It achieves State-of-the-Art (SoTA) results on object-focused datasets even when trained solely on the target domain using millions of unlabeled images.
The research explores using trained generative models (like diffusion models or GANs) to "teach" standard image backbones through . Key takeaways from the paper include: ADE20K (semantic segmentation)
: The authors investigate distilling internal generative features onto target image backbones and distilling labels obtained from generative networks with task heads onto target logits.