AGAN is the first neural architecture search method for GANs that discovers architectures outperforming state-of-the-art on CIFAR-10 unsupervised image generation and competitive on supervised tasks.
hub
Unpaired image-to-image translation using cycle-consistent adversarial networks
18 Pith papers cite this work. Polarity classification is still indexing.
18
Pith papers citing it
hub tools
citation-role summary
background 1
baseline 1
citation-polarity summary
representative citing papers
A new large-scale synthetic multi-task benchmark dataset supplying pixel-perfect depth, domain-shifted night imagery, and multi-scale low-resolution pairs for aerial remote sensing.
Progressive growing stabilizes GAN training to produce high-resolution images of unprecedented quality and achieves a record unsupervised inception score of 8.80 on CIFAR10.
A blind super-resolution method with attention-guided domain adaptation improves multispectral image quality for dead tree segmentation using unpaired data, achieving Dice scores of 54-64%.
Error distributions estimated from text repetitions enable training of denoising autoencoders that improve OCR post-correction on historical Finnish newspapers without manual training data.
A GAN with Wasserstein discriminator objective makes the generator follow the W2 geodesic to learn an optimal transport map.
MMD GANs have unbiased critic gradients but biased generator gradients from sample-based learning, and the Kernel Inception Distance provides a practical new measure for GAN convergence and dynamic learning rate adaptation.
SACRED performs unsupervised susceptibility distortion correction of EPI fMRI via image translation-based registration between T1w and unidirectional BOLD images, with test-time adaptation for robustness.
Hist2Style introduces a lightweight bilateral-grid network conditioned on histogram embeddings for distilling large-model stylization into real-time, structure-preserving, user-controllable photorealistic edits.
Experiments on real industrial time series show that partial model sharing improves diffusion model performance in bandwidth-limited non-IID settings, while full sharing stabilizes GAN training but offers less robustness than VAE or DDPM alternatives.
Three style-based neural architectures are proposed for real-time weather classification from images, with two truncated ResNet variants claimed to outperform prior methods and generalize across public datasets.
ST-STORM introduces a dual-branch SSL framework that disentangles semantic content from stylistic appearance using gated latent streams, JEPA for content invariance, and adversarial constraints for style capture.
Lightweight multi-task models using Gram matrices and PatchGAN-style architectures detect 53 weather classes from RGB images with F1 scores above 96% internally and 78% zero-shot externally, supported by a new 503k-image dataset.
MMI-ALI extends pairwise ALI models into an m-domain ensemble by maximizing MMI on joint variables to achieve scalable joint distribution matching with linear scaling in m.
DMT uses identity and makeup encoders in a GAN to enable controllable makeup transfer from references and sampling of new styles from a prior distribution.
A lightweight hybrid CNN-Transformer framework for heterogeneous face recognition achieves competitive performance on cross-spectral benchmarks and standard RGB tasks using contrastive alignment and distillation.
SAGE-GAN integrates a self-attention U-Net into a CycleGAN framework to generate realistic synthetic electron microscopy image-mask pairs that augment training data for nanoparticle segmentation without human labeling.
Hierarchical seq2seq model for parallel voice conversion pretrained as autoencoder on single-speaker data then adapted to limited multispeaker data, using mel spectrograms converted via wavenet vocoder.
citing papers explorer
-
AGAN: Towards Automated Design of Generative Adversarial Networks
AGAN is the first neural architecture search method for GANs that discovers architectures outperforming state-of-the-art on CIFAR-10 unsupervised image generation and competitive on supervised tasks.
-
SyMTRS: Benchmark Multi-Task Synthetic Dataset for Depth, Domain Adaptation and Super-Resolution in Aerial Imagery
A new large-scale synthetic multi-task benchmark dataset supplying pixel-perfect depth, domain-shifted night imagery, and multi-scale low-resolution pairs for aerial remote sensing.
-
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Progressive growing stabilizes GAN training to produce high-resolution images of unprecedented quality and achieves a record unsupervised inception score of 8.80 on CIFAR10.
-
Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation
A blind super-resolution method with attention-guided domain adaptation improves multispectral image quality for dead tree segmentation using unpaired data, achieving Dice scores of 54-64%.
-
Leveraging Text Repetitions and Denoising Autoencoders in OCR Post-correction
Error distributions estimated from text repetitions enable training of denoising autoencoders that improve OCR post-correction on historical Finnish newspapers without manual training data.
-
Adversarial Computation of Optimal Transport Maps
A GAN with Wasserstein discriminator objective makes the generator follow the W2 geodesic to learn an optimal transport map.
-
Demystifying MMD GANs
MMD GANs have unbiased critic gradients but biased generator gradients from sample-based learning, and the Kernel Inception Distance provides a practical new measure for GAN convergence and dynamic learning rate adaptation.
-
Unsupervised Susceptibility Distortion Correction of EPI without Calibration Scans via Image Translation-Based Registration
SACRED performs unsupervised susceptibility distortion correction of EPI fMRI via image translation-based registration between T1w and unidirectional BOLD images, with test-time adaptation for robustness.
-
Hist2Style: Histogram-Guided Stylization with Bilateral Grids
Hist2Style introduces a lightweight bilateral-grid network conditioned on histogram embeddings for distilling large-model stylization into real-time, structure-preserving, user-controllable photorealistic edits.
-
On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems
Experiments on real industrial time series show that partial model sharing improves diffusion model performance in bandwidth-limited non-IID settings, while full sharing stabilizes GAN training but offers less robustness than VAE or DDPM alternatives.
-
Style-Based Neural Architectures for Real-Time Weather Classification
Three style-based neural architectures are proposed for real-time weather classification from images, with two truncated ResNet variants claimed to outperform prior methods and generalize across public datasets.
-
Stylistic-STORM (ST-STORM) : Perceiving the Semantic Nature of Appearance
ST-STORM introduces a dual-branch SSL framework that disentangles semantic content from stylistic appearance using gated latent streams, JEPA for content invariance, and adversarial constraints for style capture.
-
Heuristic Style Transfer for Real-Time, Efficient Weather Attribute Detection
Lightweight multi-task models using Gram matrices and PatchGAN-style architectures detect 53 weather classes from RGB images with F1 scores above 96% internally and 78% zero-shot externally, supported by a new 503k-image dataset.
-
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
MMI-ALI extends pairwise ALI models into an m-domain ensemble by maximizing MMI on joint variables to achieve scalable joint distribution matching with linear scaling in m.
-
Disentangled Makeup Transfer with Generative Adversarial Network
DMT uses identity and makeup encoders in a GAN to enable controllable makeup transfer from references and sampling of new styles from a prior distribution.
-
Lightweight Cross-Spectral Face Recognition via Contrastive Alignment and Distillation
A lightweight hybrid CNN-Transformer framework for heterogeneous face recognition achieves competitive performance on cross-spectral benchmarks and standard RGB tasks using contrastive alignment and distillation.
-
SAGE-GAN: Towards Realistic and Robust Segmentation of Spatially Ordered Nanoparticles via Attention-Guided GANs
SAGE-GAN integrates a self-attention U-Net into a CycleGAN framework to generate realistic synthetic electron microscopy image-mask pairs that augment training data for nanoparticle segmentation without human labeling.
-
Hierarchical Sequence to Sequence Voice Conversion with Limited Data
Hierarchical seq2seq model for parallel voice conversion pretrained as autoencoder on single-speaker data then adapted to limited multispeaker data, using mel spectrograms converted via wavenet vocoder.