CIFAR-10
Canonical166papers using it
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2016first seen
60,000 32×32 color images in 10 classes — a small, standard image-classification benchmark.
Papers using CIFAR-10 (166)
- Simplified and Generalized Masked Diffusion for Discrete DataWavelet-based Variational Autoencoders for High-Resolution Image
GenerationDiffusion Models with Deterministic Normalizing Flow PriorsIs Noise Conditioning Necessary for Denoising Generative Models?Simplifying, Stabilizing and Scaling Continuous-Time Consistency ModelsLearning Discrete Autoregressive Priors with Wasserstein Gradient FlowODE-free Neural Flow Matching for One-Step Generative ModelingDiscrete Meanflow Training CurriculumDiffEnc: Variational Diffusion with a Learned EncoderUnlearning for One-Step Generative Models via Unbalanced Optimal TransportEfficient Coarse-to-Fine Diffusion Models with Time Step Sequence RedistributionVariational Trajectory Optimization of Anisotropic Diffusion SchedulesExpanding the Role of Diffusion Models for Robust Classifier TrainingU-Turn DiffusionMultilevel and Sequential Monte Carlo for Training-Free Diffusion GuidanceRethinking Refinement: Correcting Generative Bias without Noise InjectionMultivariate Variational AutoencoderFrom Diffusion to One-Step Generation: A Comparative Study of Flow-Based Models with Application to Image InpaintingOne-step Diffusion Models with Bregman Density Ratio MatchingScaling Non-Parametric Sampling with RepresentationBalanced conic rectified flowLowDiff: Efficient Diffusion Sampling with Low-Resolution ConditionModulated Diffusion: Accelerating Generative Modeling with Modulated QuantizationVariational Rank Reduction AutoencodersLearning to Integrate Diffusion ODEs by Averaging the DerivativesBidirectional Variational AutoencodersBeyond Masked and Unmasked: Discrete Diffusion Models via Partial MaskingUni-Instruct: One-step Diffusion Model through Unified Diffusion Divergence InstructionHow Do Diffusion Models Improve Adversarial Robustness?A Hybrid Wavelet-Fourier Method for Next-Generation Conditional
Diffusion ModelsVariational Self-Supervised LearningDirect Discriminative Optimization: Your Likelihood-Based Visual Generative Model is Secretly a GAN DiscriminatorInductive Moment MatchingDebiasing Kernel-Based Generative ModelsStochastic Forward-Backward Deconvolution: Training Diffusion Models with Finite Noisy DatasetsScore-of-Mixture Training: Training One-Step Generative Models Made Simple via Score Estimation of Mixture DistributionsVCT: Training Consistency Models with Variational Noise CouplingDiffusion or Non-Diffusion Adversarial Defenses: Rethinking the Relation between Classifier and Adversarial PurifierLearning to Discretize Denoising Diffusion ODEsGenerative Modelling with High-Order Langevin DynamicsDenoising Diffusion Probabilistic ModelsSpectral Normalization for Generative Adversarial NetworksImproved Training of Wasserstein GANsTraining Generative Adversarial Networks with Limited DataElucidating the Design Space of Diffusion-Based Generative ModelsMMD GAN: Towards Deeper Understanding of Moment Matching NetworkProgressive Distillation for Fast Sampling of Diffusion ModelsTackling the Generative Learning Trilemma with Denoising Diffusion GANsViTGAN: Training GANs with Vision TransformersFlow-GAN: Combining Maximum Likelihood and Adversarial Learning in
Generative ModelsKnowledge Distillation in Iterative Generative Models for Improved
Sampling SpeedCompressing GANs using Knowledge DistillationImproving the Improved Training of Wasserstein GANs: A Consistency Term
and Its Dual EffectConsistency Regularization for Generative Adversarial NetworksMaximum Likelihood Training of Score-Based Diffusion ModelsDiffuseVAE: Efficient, Controllable and High-Fidelity Generation from
Low-Dimensional LatentsEmerging Convolutions for Generative Normalizing FlowsEnsembles of Generative Adversarial NetworksAccelerating Diffusion Models via Early Stop of the Diffusion ProcessBanach Wasserstein GANClass-Splitting Generative Adversarial NetworksConsistency ModelsMixed batches and symmetric discriminators for GAN trainingQuality Aware Generative Adversarial NetworksLatent Denoising Diffusion GAN: Faster sampling, Higher image qualityMetropolis-Hastings Generative Adversarial NetworksArtGAN: Artwork Synthesis with Conditional Categorical GANsDiscriminator optimal transportFast Sampling of Diffusion Models via Operator LearningSemi-Supervised Learning with GANs: Revisiting Manifold RegularizationImproving the Speed and Quality of GAN by Adversarial TrainingManifold regularization with GANs for semi-supervised learningWays of Conditioning Generative Adversarial NetworksTraining Generative Adversarial Networks by Solving Ordinary
Differential EquationsVariational Laplace AutoencodersTRACT: Denoising Diffusion Models with Transitive Closure
Time-DistillationUnbiased Auxiliary Classifier GANs with MINEGenerative Adversarial Network based on Resnet for Conditional Image
RestorationLikelihood Estimation for Generative Adversarial NetworksOn Distillation of Guided Diffusion ModelsInvestigating Under and Overfitting in Wasserstein Generative
Adversarial NetworksData-Efficient GAN Training Beyond (Just) Augmentations: A Lottery
Ticket PerspectiveExploring Vision Transformers as Diffusion LearnersDenoising Diffusion Autoencoders are Unified Self-supervised LearnersScore-based Denoising Diffusion with Non-Isotropic Gaussian Noise ModelsOne-Step Diffusion Distillation through Score Implicit MatchingFirst Order Generative Adversarial NetworksWhy Are Conditional Generative Models Better Than Unconditional Ones?MMGAN: Manifold Matching Generative Adversarial NetworkImproved ArtGAN for Conditional Synthesis of Natural Image and ArtworkMultimodal Controller for Generative ModelsScore-Guided Generative Adversarial NetworksRobust Classification via a Single Diffusion ModelInterpreting and Improving Diffusion Models from an Optimization
PerspectiveBayesian GANToward Joint Image Generation and Compression using Generative
Adversarial NetworksPriorGAN: Real Data Prior for Generative Adversarial NetsAdaptive Weighted Discriminator for Training Generative Adversarial
NetworksHow good is my GAN?Training Wasserstein GANs without gradient penaltiesVariational Autoencoders Without the VariationSubspace Diffusion Generative ModelsA Flexible Diffusion ModelWavelet Diffusion Models are fast and scalable Image GeneratorsDuDGAN: Improving Class-Conditional GANs via Dual-DiffusionWasserstein Convergence Guarantees for a General Class of Score-Based
Generative ModelsPurify Unlearnable Examples via Rate-Constrained Variational
AutoencodersDirectly Denoising Diffusion ModelsTeaching a GAN What Not to LearnLearning Robust Variational Information Bottleneck with ReferenceScalable Variational Gaussian Processes via Harmonic Kernel
DecompositionConditional Variational Autoencoder with Balanced Pre-training for
Generative Adversarial NetworksSPI-GAN: Denoising Diffusion GANs with Straight-Path InterpolationsOn the Relationship Between Variational Inference and Auto-Associative
MemoryAccelerating Diffusion Sampling with Classifier-based Feature
DistillationClinically Relevant Latent Space Embedding of Cancer Histopathology
Slides through Variational Autoencoder Based Image CompressionGenerative Modeling through the Semi-dual Formulation of Unbalanced
Optimal TransportSimultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with
Analytical Image AttenuationStructural Adversarial Objectives for Self-Supervised Representation
LearningNeural Diffusion ModelsMimicDiffusion: Purifying Adversarial Perturbation via Mimicking Clean
Diffusion ModelSDiT: Spiking Diffusion Model with TransformerA Gauss-Newton Approach for Min-Max Optimization in Generative
Adversarial NetworksDeep MMD Gradient Flow without adversarial trainingOn Kernel-based Variational AutoencoderHierarchical VAE with a Diffusion-based VampPriorLabel-Removed Generative Adversarial Networks Incorporating with K-MeansFast Flow Reconstruction via Robust Invertible nxn ConvolutionBridging the Gap Between $f$-GANs and Wasserstein GANsEXoN: EXplainable encoder NetworkA Unified Generative Adversarial Network Training via Self-Labeling and
Self-AttentionFeature Alignment as a Generative ProcessGenerative Adversarial Learning via Kernel Density DiscriminationMomentum Contrastive Autoencoder: Using Contrastive Learning for Latent
Space Distribution Matching in WAEAn Empirical Study on GANs with Margin Cosine Loss and Relativistic
DiscriminatorGenerative Convolution Layer for Image GenerationAutoregressive Generative Modeling with Noise Conditional Maximum
Likelihood EstimationFast Inference in Denoising Diffusion Models via MMD FinetuningInterpretable ODE-style Generative Diffusion Model via Force Field
ConstructionLearning Symbolic Representations Through Joint GEnerative and
DIscriminative TrainingUDPM: Upsampling Diffusion Probabilistic ModelsA Unifying Generator Loss Function for Generative Adversarial NetworksScore Mismatching for Generative ModelingGenerative Autoencoding of Dropout PatternsACT-Diffusion: Efficient Adversarial Consistency Training for One-step
Diffusion ModelsCompensation Sampling for Improved Convergence in Diffusion ModelsOn Inference Stability for Diffusion ModelsTowards Fast Stochastic Sampling in Diffusion Generative ModelsRethinking cluster-conditioned diffusion models for label-free image
synthesisSimple Drop-in LoRA Conditioning on Attention Layers Will Improve Your
Diffusion ModelMCGAN: Enhancing GAN Training with Regression-Based Generator LossVariational Potential Flow: A Novel Probabilistic Framework for
Energy-Based Generative ModellingMachine Unlearning using a Multi-GAN based ModelPruning then Reweighting: Towards Data-Efficient Training of Diffusion
ModelsAccelerating Diffusion Models with One-to-Many Knowledge DistillationYour Image is Secretly the Last Frame of a Pseudo VideoAdvancing Diffusion Models: Alias-Free Resampling and Enhanced
Rotational EquivarianceGenerative Adversarial Networks using Adaptive ConvolutionCapsuleGAN: Generative Adversarial Capsule NetworkImage Colorization with Generative Adversarial NetworksSemi-supervised learning with Bidirectional GANsGenerative Models from the perspective of Continual LearningRobust Generative Adversarial NetworkEnhanced Balancing GAN: Minority-class Image GenerationHGAN: Hybrid Generative Adversarial Networkon the effectiveness of generative adversarial network on anomaly
detection