Improved Boundary Equilibrium Generative Adversarial Networks
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Summary
This work proposes an effective approach to generate images with higher quality and better diversity in BEGANs framework by adding a second loss function (a denoising loss) to the discriminator, so that the ability of discriminator in distinguishing between real and generated images is improved.
- Type
- article
- Published
- 2018-02-09
- Cited by
- 42
- References
- 27
- Access
- Open access
- OpenAlex
- https://openalex.org/W2790951344
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3936209
Keywords
Discriminator, Normalization (sociology), Computer science, Artificial intelligence, Generator (circuit theory)
References
- Deep Learning Face Attributes in the Wild
- Stochastic Backpropagation and Approximate Inference in Deep Generative Models
- Auto-Encoding Variational Bayes
- Extracting and composing robust features with denoising autoencoders
- Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
- Deep multi-scale video prediction beyond mean square error
- Generative Adversarial Text to Image Synthesis
- Image-to-Image Translation with Conditional Adversarial Networks
- NIPS 2016 Tutorial: Generative Adversarial Networks
- Least Squares Generative Adversarial Networks
- BEGAN: Boundary Equilibrium Generative Adversarial Networks
- Improving Generative Adversarial Networks with Denoising Feature Matching
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Amortised MAP Inference for Image Super-resolution
- Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
- Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
- Energy-based Generative Adversarial Network
- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
Cited by
- Controllable Generative Adversarial Network
- Improved generative adversarial networks with reconstruction loss
- CAAE++: Improved CAAE for Age Progression/Regression
- Recent Advances of Generative Adversarial Networks in Computer Vision
- Exploration and Exploitation of New Knowledge Emergence to Improve the Collective Intelligent Decision-Making Level of Web-of-Cells With Cyber-Physical-Social Systems Based on Complex Network Modeling
- Logo Generation with Generative Adversarial Networks Conditioned on Sentiment Terms
- Research on extraction and reproduction of deformation camouflage spot based on generative adversarial network model
- Generative Adversarial Networks Based on Denoising and Reconstruction Regularization
- Research Progress in Image Denoising Algorithms Based on Deep Learning
- Deep Automodulators
- Data augmentation using MG-GAN for improved cancer classification on gene expression data
- Face attribute editing based on generative adversarial networks
- Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review
- Cooperative Coupled Generative Networks for Generalized Zero-Shot Learning
- Deepfake Forensics, an AI-synthesized Detection with Deep Convolutional Generative Adversarial Networks
- Generation of High-resolution Lung Computed Tomography Images using Generative Adversarial Networks
- FPGAN: Face de-identification method with generative adversarial networks for social robots
- A Survey on the Progression and Performance of Generative Adversarial Networks
- Restoring Latent Vectors From Generative Adversarial Networks Using Genetic Algorithms
- Why are Generative Adversarial Networks so Fascinating and Annoying?
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