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Intra-class Variation Isolation in Conditional GANs [article]

Richard T. Marriott, Sami Romdhani, Liming Chen
2018 arXiv   pre-print
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

Vignesh Sampath, Iñaki Maurtua, Juan José Aguilar Martín, Aitor Gutierrez
2021 Journal of Big Data  
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]

Enmao Diao, Jie Ding, Vahid Tarokh
2021 arXiv   pre-print
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]

Ligong Han, Anastasis Stathopoulos, Tao Xue, Dimitris Metaxas
2020 arXiv   pre-print
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]

Itamar Winter, Daphna Weinshall
2020 arXiv   pre-print
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

Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri, Philip H.S. Torr, Puneet K. Dokania
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
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]

Augustus Odena, Jacob Buckman, Catherine Olsson, Tom B. Brown, Christopher Olah, Colin Raffel, Ian Goodfellow
2018 arXiv   pre-print
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

Run-Peng Wei, Z. Luo, B. Fang
2014 Silvae Genetica  
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]

Xu Chen, Jie Song, Otmar Hilliges
2019 arXiv   pre-print
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]

Anirudh Chandrashekar, Ashok Handa, Natesh Shivakumar, Pierfrancesco Lapolla, Vicente Grau, Regent Lee
2020 arXiv   pre-print
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

Guo-Jun Qi, Liheng Zhang, Hao Hu, Marzieh Edraki, Jingdong Wang, Xian-Sheng Hua
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
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]

Arnab Ghosh and Viveka Kulharia and Vinay Namboodiri and Philip H. S. Torr and Puneet K. Dokania
2018 arXiv   pre-print
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]

Guo-Jun Qi, Liheng Zhang, Hao Hu, Marzieh Edraki, Jingdong Wang and Xian-Sheng Hua
2018 arXiv   pre-print
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]

Lili Pan, Shen Cheng, Jian Liu, Yazhou Ren, Zenglin Xu
2019 arXiv   pre-print
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

Wang Xi, Guillaume Devineau, Fabien Moutarde, Jie Yang
2020 Algorithms  
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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