Does Diffusion Beat GAN in Image Super Resolution?

Публикации

?

Does Diffusion Beat GAN in Image Super Resolution?

P. 1–30.

Denis Kuznedelev, Valerii Startsev, Daniil Shlenskii, Dylov D., Kastryulin S.

Язык: английский

DOI

Текст на другом сайте

В книге

The Thirteenth International Conference on Learning Representations: ICLR 2025

ICLR, 2025.

Optimizing Computational Infrastructure for Large Language Models in Bioinformatics: A Case Study

Бекназаров Н. С., , in: Parallel Computational Technologies, 19th International Conference, PCT 2025, Moscow, Russia, April 8–10, 2025, Revised Selected Papers. (CCIS, volume 2891)Vol. 2891.: Springer, 2026. P. 3–16.

Добавлено: 19 мая 2026 г.

KMHCR: A Key-Controlled Signal-Domain Transformation for 5G IoT Security

Ronglin Z., Wei L., Jiahong C. и др., Journal of Signal Processing Systems 2026 Vol. 98 Article 31

Добавлено: 16 мая 2026 г.

Granular computing-based deep learning for text classification

Бехзадидуст Р., Mahan F., Izadkhah H., Information Sciences 2024 Vol. 652 Article 119746

Добавлено: 12 марта 2026 г.

Learning to hear broken motors: Signature-guided data augmentation for induction motor diagnostics

Али С., Хижик А. И., Svirin S. и др., Engineering Applications of Artificial Intelligence 2025 Vol. 170 Article 114137

The application of machine learning algorithms in the intelligent diagnosis of three-phase engine has the potential to significantly enhance diagnostic performance and accuracy. Traditional methods largely rely on signature analysis, which, despite being a standard practice, can benefit from the integration of advanced machine learning techniques. In our study, we innovate by combining machine learning ...

Добавлено: 16 февраля 2026 г.

Early warning system for Russian stock market crises: TCN-LSTM-Attention model using imbalanced data and attention mechanism

Теплова Т. В., Файзулин М. С., Куркин А. В., Socio-Economic Planning Sciences 2025 No. 101 Article 102292

Добавлено: 2 августа 2025 г.

Analyzing and forecasting P/E ratios using investor sentiment in panel data regression and LSTM models

Dolaeva A., Beliaeva U., Dmitry Grigoriev и др., International Review of Economics and Finance 2025 Vol. 98 Article 103840

Добавлено: 11 июля 2025 г.

OCEAN-AI framework with EmoFormer cross-hemiface attention approach for personality traits assessment

Elena Ryumina, Markitantov M., Dmitry Ryumin и др., Expert Systems with Applications 2024 Vol. 239 P. 0

Добавлено: 6 марта 2025 г.

Gated Siamese Fusion Network based on multimodal deep and hand-crafted features for personality traits assessment

Elena Ryumina, Markitantov M., Dmitry Ryumin и др., Pattern Recognition Letters 2024 Vol. 185 P. 45–51

Добавлено: 6 марта 2025 г.

3D convolutional neural networks for stalled brain capillary detection

Соловьёв Р. А., Габрушева Т., Калинин А., Computers in Biology and Medicine 2022 Vol. 141 P. 105–113

Добавлено: 15 января 2025 г.

The LHCb ultra-fast simulation option, Lamarr design and validation

Деркач Д. А., Казеев Н. А., Мохненко С. Н. и др., EPJ Web of Conferences 2024 Vol. 295 P. 03040

Добавлено: 8 января 2025 г.

Calibrating for the Future: Enhancing Calorimeter Longevity with Deep Learning

Али С., Рыжиков А. С., Деркач Д. А. и др., Moscow University Physics Bulletin 2024 Vol. 79 No. Suppl. 2 P. S591–S597

In the realm of high-energy physics, the longevity of calorimeters is paramount. Our research introduces a deep learning strategy to refine the calibration process of calorimeters used in particle physics experiments. We develop a Wasserstein GAN inspired methodology that adeptly calibrates the misalignment in calorimeter data due to aging or other factors. Leveraging the Wasserstein ...

Добавлено: 7 ноября 2024 г.

The Devil is in the Details: StyleFeatureEditor for Detail-Rich StyleGAN Inversion and High Quality Image Editing

Bobkov D., Titov V., Аланов А. и др., , in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.: IEEE, 2024. P. 9337–9346.

The task of manipulating real image attributes through StyleGAN inversion has been extensively researched. This process involves searching latent variables from a well-trained StyleGAN generator that can synthesize a real image modifying these latent variables and then synthesizing an image with the desired edits. A balance must be struck between the quality of the reconstruction ...

Добавлено: 10 июля 2024 г.

StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation

Аланов А., Titov V., Nakhodnov M. и др., , in: 2023 IEEE/CVF International Conference on Computer Vision (ICCV).: IEEE, 2023. P. 2184–2194.

Добавлено: 21 июня 2023 г.