연구동향 분석

보조 분류기를 이용한 GAN 모델에서의데이터 증강 누출 방지 기법

심종화 이지은 황인준 2022년

논문상세정보
인용/피인용
' 보조 분류기를 이용한 GAN 모델에서의데이터 증강 누출 방지 기법' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • GAN
  • Augmentation Leak
  • Auxiliary Classifier
  • Data augmentation
  • Deep Learning
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
1,979 0

0.0%

' 보조 분류기를 이용한 GAN 모델에서의데이터 증강 누출 방지 기법' 의 참고문헌

  • Wasserstein generative adversarial networks
    M. Arjovsky [2017]
  • Unsupervised representation learning with deep convolutional generative adversarial networks
  • Unrolled generative adversarial networks
  • Training generative adversarial networks with limited data. Advances in Neural Information Processing Systems
    T. Karras [2020]
  • The effectiveness of data augmentation in image classification using deep learning
  • Spectral normalization for generative adversarial networks
  • Revisiting self-supervised visual representation learning
  • Revisiting data augmentation for rotational invariance in convolutional neural networks
    F. Quiroga [2018]
  • Rethinking the Inception Architecture for Computer Vision
    C. Szegedy [2016]
  • Progressive growing of GANs for improved quality, stability, and variation
  • PacGAN : The power of two samples in generative adversarial networks
    Z. Lin [2018]
  • On Data Augmentation for GAN Training
  • Least squares generative adversarial networks
    X. Mao [2017]
  • Learning Deep Features for Discriminative Localization
    B. Zhou [2016]
  • Improving generalization and stability of generative adversarial networks
  • Improved training of wasserstein GANs
    Gulrajani [2017]
  • Improved consistency regularization for GANs
    Z. Zhao [2021]
  • Improved Techniques for Training GANs
    T. Salimans [2016]
  • Imagenet classification with deep convolutional neural networks
  • Image-toimage translation with conditional adversarial networks
    P. Isola [2017]
  • Image augmentations for GAN training
  • Generative adversarial networks:A survey toward private and secure applications
    Z. Cai [2021]
  • Generative adversarial nets
  • Gans trained by a two time-scale update rule converge to a local nash equilibrium
    M. Heusel [2017]
  • From facial parts responses to face detection: A deep learning approach
    S. Yang [2015]
  • Empirical analysis of overfitting and mode drop in GAN training
    Y. Yazici [2020]
  • Deep Residual Learning for Image Recognition
    K. He [2016]
  • Consistency regularization for generative adversarial networks
    H. Zhang [2020]
  • Binary cross entropy with deep learning technique for Image classification
    U. Ruby [2020]
  • Batch Normalization : Accelerating Deep Network Training by Reducing Internal Covariate Shift
    S. loffe [2015]
  • Adam: A Method for Stochastic Optimization
  • A survey on Image Data Augmentation for Deep Learning
  • A style-based generator architecture for generative adversarial networks
    T. Karras [2019]