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1,460 claims from 908 papers are on the record. 46 have been checked so far; the other 1,414 have no check with a result yet.

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Status: Unchecked ×Keyword: generative adversarial networks ×Clear all

9 claims from 5 papers

1. Computer Science › Advanced Image Processing Techniques

   Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

   Ledig, Theis, Huszár et al. · arXiv (Cornell University) · 2016

   The paper presents SRGAN, a generative adversarial network for single image super-resolution that aims to recover realistic textures at 4x upscaling, and reports better perceptual quality in a rating test.

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   1. UncheckedThe authors say SRGAN is, to their knowledge, the first framework able to infer photo-realistic natural images when enlarging images four times.“To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors.”
   2. UncheckedThe authors' deep residual network can recover photo-realistic textures from heavily downsampled images on public benchmark datasets.“Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks.”

2. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Temperature forecasting by deep learning methods

   Gong, Langguth, Ji et al. · Geoscientific model development · 2022

   Two deep learning models, ConvLSTM and SAVP, forecast hourly 2 m temperature over Europe for 12 hours from ERA5 data and beat persistence, though they remain less powerful than contemporary weather models.

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   1. UncheckedAdding 850 hPa temperature as a predictor improved the deep learning models' 2 m temperature forecasts, as did using a larger spatial domain.“Including the 850 hPa temperature as an additional predictor enhances the forecast quality, and the model also benefits from a larger spatial domain.”

3. Computer Science › Advanced Neural Network Applications

   Exploiting Channel Similarity for Network Pruning

   Zhao, Zhang and Ni · IEEE Transactions on Circuits and Systems for Video Technology · 2023

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   1. Unchecked“Precisely, we argue that channels revealing similar feature information have functional overlap and that each such similarity group can be reduced to a few representatives with little impact on the representational power of the model.”
   2. Unchecked“On ImageNet, our pruned ResNet-50 with 30% FLOPs reduced outperforms the original model.”

4. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   A Four‐Dimensional Variational Informed Generative Adversarial Network for Data Assimilation

   Wang, Duan, Ni et al. · Journal of Advances in Modeling Earth Systems · 2025

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   1. Unchecked“Moreover, our method demonstrates effective performance when starting from background fields of varying qualities, consistently achieving stable results.”

5. Computer Science › Advanced Neural Network Applications

   Winning Lottery Tickets in Deep Generative Models

   Kalibhat, Balaji and Feizi · arXiv (Cornell University) · 2020

   Unchecked3 claims
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   1. Unchecked“This approach effectively yields tickets with sparsity up to 99% for AutoEncoders, 93% for VAEs and 89% for GANs on CIFAR and Celeb-A datasets.”
   2. Unchecked“We also demonstrate the transferability of winning tickets across different generative models (GANs and VAEs) sharing the same architecture, suggesting that winning tickets have inductive biases that could help train a wide range of deep generative models.”
   3. Unchecked“Through early-bird tickets, we can achieve up to 88% reduction in floating-point operations (FLOPs) and 54% reduction in training time, making it possible to train large-scale generative models over tight resource constraints.”