arXiv AI
Jun 8
Scalable GANs with Transformers
arXiv:2509. 24935v3 Announce Type: replace-cross Abstract: Scalability has driven recent advances in generative modeling, yet its principles remain underexplored for adversarial learning.
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures
arXiv:2404. 06294v2 Announce Type: replace-cross Abstract: Super-Resolution (SR) is a time-hallowed image processing problem that aims to improve the quality of a Low-Resolution (LR) sample up to the standard of its High-Resolution (HR) counterpart.
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arXiv AI
Aug 11
When Latents Forget Pixels: Restoring Fidelity in Diffusion Transformer Super-Resolution
arXiv:2608. 09133v1 Announce Type: cross Abstract: Image super-resolution (SR) with large generative models has recently achieved remarkable perceptual quality, yet maintaining fidelity to the LR observation remains challenging.
arXiv AI
Jul 20
Efficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution
arXiv:2607. 15711v1 Announce Type: cross Abstract: Diffusion-based methods have achieved impressive performance in real-world image super-resolution (Real-ISR) by leveraging large pre-trained stable diffusion (SD) models as powerful generative priors.
arXiv Machine Learning
Jun 30
$\mu$Flow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors
arXiv:2606. 30528v1 Announce Type: cross Abstract: Current generative models, including GANs and diffusion models, have reached an outstanding level of photorealism, posing significant risks to privacy and security.
Hugging Face Trending Papers
Jun 8
TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution
Diffusion-based generative models have achieved remarkable success in real-world image super-resolution (SR). With tiled diffusion techniques, these models can produce high-resolution images that exceed their native-supported resolution.
arXiv AI
Jul 7
PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification
arXiv:2405. 07332v2 Announce Type: cross Abstract: Numerous applications have resulted from the automation of agricultural disease segmentation using deep learning techniques.
arXiv Machine Learning
Jul 31
Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation
arXiv:2607. 27372v1 Announce Type: new Abstract: The deep learning revolution, kicked off by AlexNet, taught us that end-to-end training beats decomposing a problem into hand-designed stages.
arXiv Machine Learning
Jun 18
Investigation of Neural Network Methods for Reconstruction and Classification of Texture Images Under Conditions of Incomplete Information
arXiv:2204. 14224v3 Announce Type: replace-cross Abstract: The automated analysis of heterogeneous natural textures is frequently hindered by physical damage and data loss, presenting a significant challenge to computer vision.
arXiv AI
Jun 9
SRT: Super-Resolution for Time Series via Disentangled Rectified Flow
arXiv:2606. 07605v1 Announce Type: cross Abstract: Fine-grained time series data with high temporal resolution is critical for accurate analytics across a wide range of applications.
arXiv Machine Learning
Jul 22
Democratizing Advanced High-Throughput Imaging via Cross-Instrument Deep Learning-Enabled Modality Transfer
arXiv:2403. 18026v3 Announce Type: replace-cross Abstract: High-throughput imaging is often constrained by a trade-off between acquisition speed and image quality.
arXiv Machine Learning
Aug 12
Deciding When to Switch: E-Processes for Adaptive Minimax Training for Generative Adversarial Nets
arXiv:2608. 10096v1 Announce Type: cross Abstract: Modern data science increasingly gives rise to hypothesis-testing problems that are not naturally formulated in terms of parameters within prespecified statistical models.
arXiv Machine Learning
Jul 8
Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization
arXiv:2508. 01725v5 Announce Type: replace Abstract: Recent advances in continuous conditional generative modeling, including Continuous conditional Generative Adversarial Network (CcGAN) and Continuous Conditional Diffusion Model (CCDM), estimate high-dimensional data distributions conditioned on scalar regression labels such as angles, ages, or temperatures.