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Intra-class Variation Isolation in Conditional GANs
[article]
2018
arXiv
pre-print
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We coin the method intra-class variation isolation (IVI) and the resulting network the IVI-GAN. ...
Current state-of-the-art conditional generative adversarial networks (C-GANs) require strong supervision via labeled datasets in order to generate images with continuously adjustable, disentangled semantics ...
Intra-class variation isolation Before describing intra-class variation isolation, we introduce the conditional GAN. ...
arXiv:1811.11296v1
fatcat:sa6cwtgucjbmzponaryw3vpiyy
A survey on generative adversarial networks for imbalance problems in computer vision tasks
2021
Journal of Big Data
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In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. ...
We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. ...
Fine-grained image classification The fine-grained image classification is also attributed to major variations in the intra-class and minor inter class variations [184] . ...
doi:10.1186/s40537-021-00414-0
pmid:33552840
pmcid:PMC7845583
fatcat:g3p6hbjuj5c5vbe23ms4g6ed6q
Multimodal Controller for Generative Models
[article]
2021
arXiv
pre-print
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We demonstrate that multimodal controlled generative models (including VAE, PixelCNN, Glow, and GAN) can generate class-conditional images of significantly better quality when compared with the state-of-the-art ...
Class-conditional generative models are crucial tools for data generation from user-specified class labels. ...
Therefore, it is difficult for a small subnetwork to learn one mode of data with a high intra-class variation. ...
arXiv:2002.02572v6
fatcat:b2cpfawyirczplfamz3yaklerq
Unbiased Auxiliary Classifier GANs with MINE
[article]
2020
arXiv
pre-print
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Auxiliary Classifier GANs (AC-GANs) are widely used conditional generative models and are capable of generating high-quality images. ...
However, it has been reported that using a twin auxiliary classifier may cause instability in training. ...
MNIST and CIFAR10
Conclusion In this paper, we reviewed the low intra-class diversity problem of the AC-GAN model. ...
arXiv:2006.07567v1
fatcat:42l2icycpnenrdsxv636a6pv5q
Multiclass non-Adversarial Image Synthesis, with Application to Classification from Very Small Sample
[article]
2020
arXiv
pre-print
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In the full data regime, our method is capable of generating diverse multi-class images with no supervision, surpassing previous non-adversarial methods in terms of image quality and diversity. ...
In this work we present a novel non-adversarial generative method - Clustered Optimization of LAtent space (COLA), which overcomes some of the limitations of GANs, and outperforms GANs when training data ...
Acknowledgements This work was supported in part by a grant from the Israel Science Foundation (ISF) and by the Gatsby Charitable Foundations. ...
arXiv:2011.12942v2
fatcat:ngazz5vo5jhg5otiun2n7pkhqe
Multi-agent Diverse Generative Adversarial Networks
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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In addition, we also show that MAD-GAN is able to disentangle different modalities when trained using highly challenging diverse-class dataset (e.g. dataset with images of forests, icebergs, and bedrooms ...
We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known problem of mode collapse. ...
Generators among themselves are able to disentangle inter-class variations, and each generator is also able to capture intra-class variations. ...
doi:10.1109/cvpr.2018.00888
dblp:conf/cvpr/GhoshKNTD18
fatcat:vds2jm3mdrhedoc45phwm7ps6m
Is Generator Conditioning Causally Related to GAN Performance?
[article]
2018
arXiv
pre-print
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Motivated by this, we study the distribution of singular values of the Jacobian of the generator in Generative Adversarial Networks (GANs). ...
Moreover, we find that the average (with z from p(z)) conditioning of the generator is highly predictive of two other ad-hoc metrics for measuring the 'quality' of trained GANs: the Inception Score and ...
We thank Ishaan Gulrajani for sharing code for a baseline CIFAR-10 GAN implementation. We thank Daniel Duckworth for help implementing an efficient Jacobian computation in TensorFlow. ...
arXiv:1802.08768v2
fatcat:z2263l4nxndpjgzkdpfnhaqlua
Clonal Variation of Eucalypts in Susceptibility to Bacterial Wilt Detected by Using Different Inoculation Methods
2014
Silvae Genetica
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The results showed that these inoculation methods obviously differed in the disease infection process, clonal variation and clonal mean repeatability in susceptibility of stock materials inoculated. ...
Four inoculation methods were investigated for assessing the clonal variation of eucalypts in susceptibility to bacterial wilt (Ralstonia solanacearum). ...
Clonal ramet based repeatability (R R ) or intra-class correlation (t) measured the fraction of the clonal or genetic variation in the total phenotypic variation (BECKER, 1992; BALTUNIS and BRAWNER, 2010 ...
doi:10.1515/sg-2014-0004
fatcat:nra6eltqrzdgnirzzpxwunezyy
Unpaired Pose Guided Human Image Generation
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1901.02284v1 fatcat:o4o4kka7jfatdhk6q6vryblgmy
The model allows to generate novel samples conditioned on either an image taken from the target domain or a class label indicating the style of clothing (e.g., t-shirt). ...
Finally, we show in a large scale perceptual study that our approach can generate realistic looking images and that participants struggle in detecting fake images versus real samples, especially if faces ...
Fig. 8 illustrates that the architecture generates images of sufficient quality and is capable of producing samples with significant intra-class variation. ...
arXiv:1901.02284v2
fatcat:ec7w4sujkbgw3lhnnp2x23ocjy
A Deep learning Approach to Generate Contrast-Enhanced Computerised Tomography Angiography without the Use of Intravenous Contrast Agents
[article]
2020
arXiv
pre-print
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Non-contrast axial slices within the AAA from 10 patients (n = 100) were sampled for the underlying Hounsfield unit (HU) distribution at the lumen, intra-luminal thrombus and interface locations. ...
Sampling of HUs in these regions revealed significant differences between all regions (p<0.001 for all comparisons), confirming the intrinsic differences in the radiomic signatures between these regions ...
Variations of the GAN architecture include the Pix2Pix [11] , and conditional [21] and cycle -GAN networks [10] . ...
arXiv:2003.01223v1
fatcat:dzkeivbngbgo5ezn5uap66pige
Global Versus Localized Generative Adversarial Nets
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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In this paper, we present a novel localized Generative Adversarial Net (GAN) to learn on the manifold of real data. ...
The locality nature of LGAN enables local generators to adapt to and directly access the local geometry without need to invert the generator in a global GAN. ...
GAN (cGAN) with x as its condition. ...
doi:10.1109/cvpr.2018.00164
dblp:conf/cvpr/QiZHEWH18
fatcat:5axtaxoehjckjciyl2nmqklcwa
Multi-Agent Diverse Generative Adversarial Networks
[article]
2018
arXiv
pre-print
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In addition, we also show that MAD-GAN is able to disentangle different modalities when trained using highly challenging diverse-class dataset (e.g. dataset with images of forests, icebergs, and bedrooms ...
We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known problem of mode collapse. ...
Generators among themselves are able to disentangle inter-class variations, and each generator is also able to capture intra-class variations. ...
arXiv:1704.02906v3
fatcat:7yxfe2yvfzgpremzmcp7hidpte
Global versus Localized Generative Adversarial Nets
[article]
2018
arXiv
pre-print
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In this paper, we present a novel localized Generative Adversarial Net (GAN) to learn on the manifold of real data. ...
The locality nature of LGAN enables local generators to adapt to and directly access the local geometry without need to invert the generator in a global GAN. ...
The results are reported in Table 4 . ...
arXiv:1711.06020v2
fatcat:olr7mkzorja4tigie2dakcf7wq
Latent Dirichlet Allocation in Generative Adversarial Networks
[article]
2019
arXiv
pre-print
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For the adversarial training, LDAGAN derives a variational expectation-maximization (VEM) algorithm to estimate model parameters. ...
In detail, for the generative process modelling, LDAGAN defines a generative mode for each sample, determining which generative sub-process it belongs to. ...
Secondly, without no structure information, some multigenerator based GANs (Hoang et al., 2018) encourage mode diversity of generated samples, resulting in intra-class mode dropping. ...
arXiv:1812.06571v5
fatcat:3c5nvdv6njeunlfeafeuy6qaey
Generative Model for Skeletal Human Movements Based on Conditional DC-GAN Applied to Pseudo-Images
2020
Algorithms
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We propose to use a conditional Deep Convolutional Generative Adversarial Network (DC-GAN) applied to pseudo-images representing skeletal pose sequences using tree structure skeleton image format. ...
To the best of our knowledge, our work is the first successful class-conditioned generative model for human skeletal motions based on pseudo-image representation of skeletal pose sequences. ...
Conditional Deep Convolution Generative Adversarial Network (Conditional DC-GAN) In this paper, we used a conditional Deep Convolution Generative Adversarial Network (conditional DC-GAN) based on DC-GAN ...
doi:10.3390/a13120319
fatcat:wcj46asfanetpdm5h3e5bfp3hm
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