Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization | Paper Citation Record

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This paper cites PcDGAN: A continuous conditional diverse generative adversarial network for inverse design,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization PcDGAN: A continuous conditional diverse generative adversarial network for inverse design,

Reference 1

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This paper cites CcDPM: A continuous conditional diffusion probabilistic model for inverse design,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization CcDPM: A continuous conditional diffusion probabilistic model for inverse design,

Reference 2

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Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Diverse 3D auxetic unit cell inverse design with deep learning,

Reference 3

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This paper cites Data augmentation using continuous conditional generative adversarial networks for regression and its application to improved spectral sensing,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Data augmentation using continuous conditional generative adversarial networks for regression and its application to improved spectral sensing,

Reference 4

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This paper cites Point cloud generation with continuous conditioning,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Point cloud generation with continuous conditioning,

Reference 5

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This paper cites Predicting the co2 propagation in geological formations from sparsely available well data,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Predicting the co2 propagation in geological formations from sparsely available well data,

Reference 6

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This paper cites GAN-based framework for unified estimation of process-induced random variation in FinFET,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization GAN-based framework for unified estimation of process-induced random variation in FinFET,

Reference 7

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This paper cites Scaling up gans for text-to-image synthesis,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Scaling up gans for text-to-image synthesis,

Reference 8

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This paper cites Image de-raining using a conditional generative adversarial network,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Image de-raining using a conditional generative adversarial network,

Reference 9

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This paper cites Statistics enhancement generative adversarial networks for diverse conditional image synthesis,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Statistics enhancement generative adversarial networks for diverse conditional image synthesis,

Reference 10

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This paper cites Scalable diffusion models with transformers,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Scalable diffusion models with transformers,

Reference 11

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This paper cites Adaptive double-branch fusion conditional diffusion model for underwater image restoration,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Adaptive double-branch fusion conditional diffusion model for underwater image restoration,

Reference 12

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This paper cites Image intrinsic components guided conditional diffusion model for low-light image enhancement,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Image intrinsic components guided conditional diffusion model for low-light image enhancement,

Reference 13

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This paper cites CCDM: Continuous Conditional Diffusion Models for Image Generation.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization CCDM: Continuous Conditional Diffusion Models for Image Generation

Reference 14

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This paper cites CcGAN: Continuous conditional generative adversarial networks for image generation,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization CcGAN: Continuous conditional generative adversarial networks for image generation,

Reference 15

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This paper cites Continuous conditional generative adversarial networks: Novel empirical losses and label input mechanisms,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Continuous conditional generative adversarial networks: Novel empirical losses and label input mechanisms,

Reference 16

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This paper cites Image generation using continuous conditional generative adver- sarial networks,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Image generation using continuous conditional generative adver- sarial networks,

Reference 17

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This paper cites Generative adversarial nets,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Generative adversarial nets,

Reference 18

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This paper cites Improved distribution matching distillation for fast image synthesis,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Improved distribution matching distillation for fast image synthesis,

Reference 19

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This paper cites Denoising diffusion implicit models,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Denoising diffusion implicit models,

Reference 20

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This paper cites Efficient subsampling of realistic images from GANs conditional on a class or a continuous variable,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Efficient subsampling of realistic images from GANs conditional on a class or a continuous variable,

Reference 21

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This paper cites Subsampling generative adversarial networks: Density ratio estimation in feature space with JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2025 12 softplus loss,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Subsampling generative adversarial networks: Density ratio estimation in feature space with JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2025 12 softplus loss,

Reference 22

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Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Turning waste into wealth: Leveraging low-quality samples for enhancing continuous conditional generative adversarial networks,

Reference 23

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This paper cites Spectral normal- ization for generative adversarial networks,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Spectral normal- ization for generative adversarial networks,

Reference 24

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This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 25

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Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Self-attention generative adversarial networks,

Reference 26

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Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Large scale GAN training for high fidelity natural image synthesis,

Reference 27

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Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Unresolved cited work

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Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Deep residual learning for image recognition,

Reference 29

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Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization f-GAN: Training generative neural samplers using variational divergence minimization,

Reference 30

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Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Analyzing and improving the training dynamics of diffusion models,

Reference 31

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Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Group normalization,

Reference 32

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This paper cites Conditional image synthesis with auxiliary classifier gans,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Conditional image synthesis with auxiliary classifier gans,

Reference 33

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Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Rebooting ACGAN: Auxiliary classifier GANs with stable training,

Reference 34

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Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Conditional GANs with auxiliary discriminative classifier,

Reference 35

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This paper cites Mixed preci- sion training,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Mixed preci- sion training,

Reference 36

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This paper cites Accelerate: Training and inference at scale made simple, efficient and adaptable.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Accelerate: Training and inference at scale made simple, efficient and adaptable

Reference 37

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This paper cites Age progression/regression by conditional adversarial autoencoder,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Age progression/regression by conditional adversarial autoencoder,

Reference 38

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This paper cites The Steering Angle dataset @ONLINE,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization The Steering Angle dataset @ONLINE,

Reference 39

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This paper cites Real-ESRGAN: Training real- world blind super-resolution with pure synthetic data,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Real-ESRGAN: Training real- world blind super-resolution with pure synthetic data,

Reference 40

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This paper cites Towards real-world blind face restoration with generative facial prior,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Towards real-world blind face restoration with generative facial prior,

Reference 41

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This paper cites Differentiable augmentation for data-efficient GAN training,.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Differentiable augmentation for data-efficient GAN training,

Reference 42

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This paper cites Making a “completely blind.

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization Making a “completely blind

Reference 43

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