A survey on generative diffusion models
Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …
capturing and generalizing patterns within data, we have entered the epoch of all …
Self-consuming generative models go mad
Seismic advances in generative AI algorithms for imagery, text, and other data types has led
to the temptation to use synthetic data to train next-generation models. Repeating this …
to the temptation to use synthetic data to train next-generation models. Repeating this …
Editanything: Empowering unparalleled flexibility in image editing and generation
Image editing plays a vital role in computer vision field, aiming to realistically manipulate
images while ensuring seamless integration. It finds numerous applications across various …
images while ensuring seamless integration. It finds numerous applications across various …
Ai-generated images as data source: The dawn of synthetic era
The advancement of visual intelligence is intrinsically tethered to the availability of data. In
parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic …
parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic …
Synthetic data as validation
This study leverages synthetic data as a validation set to reduce overfitting and ease the
selection of the best model in AI development. While synthetic data have been used for …
selection of the best model in AI development. While synthetic data have been used for …
Generative diffusion models: A survey of current theoretical developments
MN Yeğin, MF Amasyalı - Neurocomputing, 2024 - Elsevier
Generative diffusion models showed high success in many fields with a powerful theoretical
background. They convert the data distribution to noise and remove the noise back to obtain …
background. They convert the data distribution to noise and remove the noise back to obtain …
In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image Classification
To alleviate the utility degradation of deep learning image classification with differential
privacy (DP) employing extra public data or pre-trained models has been widely explored …
privacy (DP) employing extra public data or pre-trained models has been widely explored …
Theoretical research on generative diffusion models: an overview
MN Yeğin, MF Amasyalı - arXiv preprint arXiv:2404.09016, 2024 - arxiv.org
Generative diffusion models showed high success in many fields with a powerful theoretical
background. They convert the data distribution to noise and remove the noise back to obtain …
background. They convert the data distribution to noise and remove the noise back to obtain …
[HTML][HTML] Transforming the future: a review of artificial intelligence models
AA Pugachev, AV Kharchenko… - … дружбы народов. Серия …, 2023 - cyberleninka.ru
A comprehensive review of existing artificial intelligence models, focusing on fourteen
prominent language and multimodal generative models from four rapidly evolving …
prominent language and multimodal generative models from four rapidly evolving …
On the statistical complexity of sample amplification
On the statistical complexity of sample amplification Page 1 The Annals of Statistics 2024,
Vol. 52, No. 6, 2767–2790 https://doi.org/10.1214/24-AOS2444 © Institute of Mathematical …
Vol. 52, No. 6, 2767–2790 https://doi.org/10.1214/24-AOS2444 © Institute of Mathematical …