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Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to
  Probe the Boundaries of Stable Diffusion Generated Data

Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data

6 May 2024
Leonhard Hennicke
C. Adriano
Holger Giese
Jan Mathias Koehler
Lukas Schott
    DiffM
ArXivPDFHTML

Papers citing "Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data"

4 / 4 papers shown
Title
Tiny models from tiny data: Textual and null-text inversion for few-shot distillation
Tiny models from tiny data: Textual and null-text inversion for few-shot distillation
Erik Landolsi
Fredrik Kahl
DiffM
53
1
0
05 Jun 2024
SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?
SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?
Hasan Hammoud
Hani Itani
Fabio Pizzati
Philip H. S. Torr
Adel Bibi
Bernard Ghanem
CLIP
VLM
112
35
0
02 Feb 2024
Emerging Properties in Self-Supervised Vision Transformers
Emerging Properties in Self-Supervised Vision Transformers
Mathilde Caron
Hugo Touvron
Ishan Misra
Hervé Jégou
Julien Mairal
Piotr Bojanowski
Armand Joulin
295
5,761
0
29 Apr 2021
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize
  Long-Tail Visual Concepts
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
Soravit Changpinyo
P. Sharma
Nan Ding
Radu Soricut
VLM
273
1,077
0
17 Feb 2021
1