Are conditional GANs explicitly conditional?

• URL: http://arxiv.org/abs/2106.15011v1
• Date: Mon, 28 Jun 2021 22:49:27 GMT
• Title: Are conditional GANs explicitly conditional?
• Authors: Houssem-eddine Boulahbal, Adrian Voicila, Andrew Comport
• Abstract summary: This paper proposes two contributions for conditional Generative Adversarial Networks (cGANs) The first main contribution is an analysis of cGANs to show that they are not explicitly conditional. The second contribution is a new method, called acontrario, that explicitly models conditionality for both parts of the adversarial architecture.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper proposes two important contributions for conditional Generative Adversarial Networks (cGANs) to improve the wide variety of applications that exploit this architecture. The first main contribution is an analysis of cGANs to show that they are not explicitly conditional. In particular, it will be shown that the discriminator and subsequently the cGAN does not automatically learn the conditionality between inputs. The second contribution is a new method, called acontrario, that explicitly models conditionality for both parts of the adversarial architecture via a novel acontrario loss that involves training the discriminator to learn unconditional (adverse) examples. This leads to a novel type of data augmentation approach for GANs (acontrario learning) which allows to restrict the search space of the generator to conditional outputs using adverse examples. Extensive experimentation is carried out to evaluate the conditionality of the discriminator by proposing a probability distribution analysis. Comparisons with the cGAN architecture for different applications show significant improvements in performance on well known datasets including, semantic image synthesis, image segmentation and monocular depth prediction using different metrics including Fr\'echet Inception Distance(FID), mean Intersection over Union (mIoU), Root Mean Square Error log (RMSE log) and Number of statistically-Different Bins (NDB)

Related papers

• Combating Mode Collapse in GANs via Manifold Entropy Estimation [70.06639443446545]
  Generative Adversarial Networks (GANs) have shown compelling results in various tasks and applications. We propose a novel training pipeline to address the mode collapse issue of GANs.
  arXiv  Detail & Related papers  (2022-08-25T12:33:31Z)
• Training and Inference on Any-Order Autoregressive Models the Right Way [97.39464776373902]
  A family of Any-Order Autoregressive Models (AO-ARMs) has shown breakthrough performance in arbitrary conditional tasks. We identify significant improvements to be made to previous formulations of AO-ARMs. Our method leads to improved performance with no compromises on tractability.
  arXiv  Detail & Related papers  (2022-05-26T18:00:02Z)
• cGANs with Auxiliary Discriminative Classifier [43.78253518292111]
  Conditional generative models aim to learn the underlying joint distribution of data and labels. auxiliary classifier generative adversarial networks (AC-GAN) have been widely used, but suffer from the issue of low intra-class diversity on generated samples. We propose novel cGANs with auxiliary discriminative classifier (ADC-GAN) to address the issue of AC-GAN.
  arXiv  Detail & Related papers  (2021-07-21T13:06:32Z)
• Monte Carlo Simulation of SDEs using GANs [0.0]
  We investigate if GANs can also be used to approximate one-dimensional geometric Ito differential equations (SDEs) Standard GANs are only able to approximate processes in distribution, yielding a weak approximation to the SDE. A conditional GAN architecture is proposed that enables strong approximation. We compare the input-output map obtained with the standard GAN and supervised GAN and show experimentally that the standard GAN may fail to provide a path-wise approximation.
  arXiv  Detail & Related papers  (2021-04-03T16:06:30Z)
• Continuous Conditional Generative Adversarial Networks: Novel Empirical Losses and Label Input Mechanisms [26.331792743036804]
  This work proposes the continuous conditional generative adversarial network (CcGAN) CcGAN is the first generative model for image generation conditional on continuous, scalar conditions (termed regression labels)
  arXiv  Detail & Related papers  (2020-11-15T07:29:41Z)
• Autoregressive Score Matching [113.4502004812927]
  We propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariable log-conditionals (scores) For AR-CSM models, this divergence between data and model distributions can be computed and optimized efficiently, requiring no expensive sampling or adversarial training. We show with extensive experimental results that it can be applied to density estimation on synthetic data, image generation, image denoising, and training latent variable models with implicit encoders.
  arXiv  Detail & Related papers  (2020-10-24T07:01:24Z)
• Double Generative Adversarial Networks for Conditional Independence Testing [8.359770027722275]
  High-dimensional conditional independence testing is a key building block in statistics and machine learning. We propose an inferential procedure based on double generative adversarial networks (GANs)
  arXiv  Detail & Related papers  (2020-06-03T16:14:15Z)
• Inducing Optimal Attribute Representations for Conditional GANs [61.24506213440997]
  Conditional GANs are widely used in translating an image from one category to another. Existing conditional GANs commonly encode target domain label information as hard-coded categorical vectors in the form of 0s and 1s. We propose a novel end-to-end learning framework with Graph Convolutional Networks to learn the attribute representations to condition on the generator.
  arXiv  Detail & Related papers  (2020-03-13T20:24:07Z)
• GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences [70.30467057209405]
  Generative Adversarial Networks (GANs) are modern methods to learn the underlying distribution of a data set. GANs are designed in a model-free fashion where no additional information about the underlying distribution is available. We propose a principled design of a model-based GAN that uses a set of simple discriminators on the neighborhoods of the Bayes-net/MRF.
  arXiv  Detail & Related papers  (2020-03-02T04:31:22Z)
• When Relation Networks meet GANs: Relation GANs with Triplet Loss [110.7572918636599]
  Training stability is still a lingering concern of generative adversarial networks (GANs) In this paper, we explore a relation network architecture for the discriminator and design a triplet loss which performs better generalization and stability. Experiments on benchmark datasets show that the proposed relation discriminator and new loss can provide significant improvement on variable vision tasks.
  arXiv  Detail & Related papers  (2020-02-24T11:35:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.