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Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets
TL;DR: In this paper, a new approach based on using inverse generator (IG$) model as encoder and pre-trained generator (G$) as decoder of an AutoEncoder network to train the $IG$ model is proposed.
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Abstract: The inverse mapping of GANs'(Generative Adversarial Nets) generator has a great potential value.Hence, some works have been developed to construct the inverse function of generator by directly learning or adversarial learning.While the results are encouraging, the problem is highly challenging and the existing ways of training inverse models of GANs have many disadvantages, such as hard to train or poor performance.Due to these reasons, we propose a new approach based on using inverse generator ($IG$) model as encoder and pre-trained generator ($G$) as decoder of an AutoEncoder network to train the $IG$ model. In the proposed model, the difference between the input and output, which are both the generated image of pre-trained GAN's generator, of AutoEncoder is directly minimized. The optimizing method can overcome the difficulty in training and inverse model of an non one-to-one function.We also applied the inverse model of GANs' generators to image searching and translation.The experimental results prove that the proposed approach works better than the traditional approaches in image searching.
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Inverting the Generator of a Generative Adversarial Network
Antonia Creswell,Anil A. Bharath +1 more
TL;DR: This paper introduces a technique, inversion, to project data samples, specifically images, to the latent space using a pretrained GAN, and demonstrates how the proposed inversion technique may be used to quantitatively compare the performance of various GAN models trained on three image data sets.
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Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing
Hyun-Su Kim,Yunjey Choi,Junho Kim,Sungjoo Yoo,Youngjung Uh +4 more
- 01 Jun 2021
TL;DR: In this article, the intermediate latent space has spatial dimensions, and a spatially variant modulation replaces AdaIN, which makes the embedding through an encoder more accurate than existing optimization-based methods while maintaining the properties of GANs.
Image synthesis with adversarial networks: A comprehensive survey and case studies
Pourya Shamsolmoali,Pourya Shamsolmoali,Masoumeh Zareapoor,Eric Granger,Huiyu Zhou,Ruili Wang,M. Emre Celebi,Jie Yang +7 more
TL;DR: This survey provides a comprehensive review of adversarial models for image synthesis, and summarizes the synthetic image generation methods, and discusses the categories including image-to-image translation, fusion image generation, label- to-image mapping, and text-to -image translation.
YOLO in the Dark - Domain Adaptation Method for Merging Multiple Models
Yukihiro Sasagawa,Hajime Nagahara +1 more
- 23 Aug 2020
TL;DR: This work proposes a method of domain adaptation for merging multiple models with less effort than creating an additional dataset, and presents the proposed YOLOin-the-Dark model, which uses fewer computing resources than the naive approach.
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Inverting The Generator Of A Generative Adversarial Network (II)
Antonia Creswell,Anil A. Bharath +1 more
TL;DR: In this paper, the authors introduce techniques for projecting image samples into the latent space using any pre-trained GAN, provided that the computational graph is available, and evaluate these techniques on both MNIST digits and Omniglot handwritten characters.
111
References
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
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TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
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Image-to-Image Translation with Conditional Adversarial Networks
TL;DR: Conditional Adversarial Network (CA) as discussed by the authors is a general-purpose solution to image-to-image translation problems, which can be used to synthesize photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
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Deep Learning Face Attributes in the Wild
Ziwei Liu,Ping Luo,Xiaogang Wang,Xiaoou Tang +3 more
- 07 Dec 2015
TL;DR: A novel deep learning framework for attribute prediction in the wild that cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently.
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Learning Deep Architectures for AI
Yoshua Bengio
- 01 Jan 2009
TL;DR: The motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer modelssuch as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks are discussed.
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
TL;DR: This work introduces a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrates that they are a strong candidate for unsupervised learning.
6.8K
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