Add to ORKGInternational audienceRecent image generation models such as Stable Diffusion have exhibited an impressive ability to generate fairly realistic images starting from a simple text prompt. Could such models render real images obsolete for training image prediction models? In this paper, we answer part of this provocative question by investigating the need for real images when training models for ImageNet classification.Provided only with the class names that have been used to build the dataset, we explore the ability of Stable Diffusionto generate synthetic clones of ImageNet and measure how useful these are for training classification models from scratch. We show that with minimal and class-agnostic prompt engineering, ImageNet clones are ab...
Supervised training of deep neural networks requires a large amount of training data. Since labeling...
Current vision systems are trained on huge datasets, and these datasets come with costs: curation is...
Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained so...
Recent large-scale image generation models such as Stable Diffusion have exhibited an impressive abi...
We generate synthetic images with the "Stable Diffusion" image generation model using the Wordnet ta...
Deep learning applications on computer vision involve the use of large-volume and representative dat...
Models trained on synthetic images often face degraded generalization to real data. As a convention,...
Creating big datasets is often difficult or expensive which causes people to augment their dataset w...
Detecting fake images is becoming a major goal of computer vision. This need is becoming more and mo...
ImageNet pre-training has enabled state-of-the-art results on many tasks. In spite of its recognized...
Machine Learning is a fast growing area that revolutionizes computer programs by providing systems w...
In this study, we introduce a novel pipeline for synthetic data generation of textured surfaces, mot...
Modern diffusion models have set the state-of-the-art in AI image generation. Their success is due, ...
The tremendous success of neural networks is clouded by the existence of adversarial examples: malic...
Realistic synthetic image data rendered from 3D models can be used to augment image sets and train i...
Supervised training of deep neural networks requires a large amount of training data. Since labeling...
Current vision systems are trained on huge datasets, and these datasets come with costs: curation is...
Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained so...
Recent large-scale image generation models such as Stable Diffusion have exhibited an impressive abi...
We generate synthetic images with the "Stable Diffusion" image generation model using the Wordnet ta...
Deep learning applications on computer vision involve the use of large-volume and representative dat...
Models trained on synthetic images often face degraded generalization to real data. As a convention,...
Creating big datasets is often difficult or expensive which causes people to augment their dataset w...
Detecting fake images is becoming a major goal of computer vision. This need is becoming more and mo...
ImageNet pre-training has enabled state-of-the-art results on many tasks. In spite of its recognized...
Machine Learning is a fast growing area that revolutionizes computer programs by providing systems w...
In this study, we introduce a novel pipeline for synthetic data generation of textured surfaces, mot...
Modern diffusion models have set the state-of-the-art in AI image generation. Their success is due, ...
The tremendous success of neural networks is clouded by the existence of adversarial examples: malic...
Realistic synthetic image data rendered from 3D models can be used to augment image sets and train i...
Supervised training of deep neural networks requires a large amount of training data. Since labeling...
Current vision systems are trained on huge datasets, and these datasets come with costs: curation is...
Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained so...