You can access the full paper through the following sources: OpenAccess (TheCVF) arXiv Preprint IEEE Xplore
The research explores using trained generative models (like diffusion models or GANs) to "teach" standard image backbones through . Key takeaways from the paper include: DreamTeacher-Ep1Pt1.2-pc_[juegosXXXgratis.com].zip
The primary scientific paper related to is titled "DreamTeacher: Pretraining Image Backbones with Deep Generative Models" , published at ICCV 2023 . You can access the full paper through the
: 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. 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.
: DreamTeacher significantly outperforms existing self-supervised learning approaches on benchmarks like ImageNet , ADE20K (semantic segmentation), and MSCOCO (instance segmentation).
[2307.07487] DreamTeacher: Pretraining Image Backbones with Deep Generative Models.