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Generative Adversarial Networks
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Completely Automated CNN Architecture Design Based on Blocks
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Novel Prediction Strategies for Dynamic Multiobjective Optimization
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Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor
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Re-purposing heterogeneous generative ensembles with evolutionary computation
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microbatchGAN: Stimulating Diversity with Multi-Adversarial Discrimination
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CatGAN: Category-aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text Generation
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COEGAN: evaluating the coevolution effect in generative adversarial networks
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Deep Neuro-Cognitive Co-Evolution for Fuzzy Attribute Reduction by Quantum Leaping PSO With Nearest-Neighbor Memeplexes
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Spatial evolutionary generative adversarial networks
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A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model
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Dynamic Cooperative Coevolution for Large Scale Optimization
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Multi-objective training of Generative Adversarial Networks with multiple discriminators
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Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification
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Online Adaptative Curriculum Learning for GANs
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Towards Distributed Coevolutionary GANs
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Cooperative Co-Evolution-Based Design Optimization: A Concurrent Engineering Perspective
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Evolutionary Generative Adversarial Networks
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MGAN: Training Generative Adversarial Nets with Multiple Generators
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Spectral Normalization for Generative Adversarial Networks
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On the Effectiveness of Least Squares Generative Adversarial Networks
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A Particle Swarm Optimization-Based Flexible Convolutional Autoencoder for Image Classification
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Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations
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Evolving Deep Convolutional Neural Networks for Image Classification
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Dual Discriminator Generative Adversarial Nets
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MoCoGAN: Decomposing Motion and Content for Video Generation
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Wasserstein Generative Adversarial Networks
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
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Gradient descent GAN optimization is locally stable
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Stabilizing GAN Training with Multiple Random Projections
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Multi-agent Diverse Generative Adversarial Networks
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Improved Training of Wasserstein GANs
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BEGAN: Boundary Equilibrium Generative Adversarial Networks
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Generalization and Equilibrium in Generative Adversarial Nets (GANs)
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Towards Principled Methods for Training Generative Adversarial Networks
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AdaGAN: Boosting Generative Models
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NIPS 2016 Tutorial: Generative Adversarial Networks
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Least Squares Generative Adversarial Networks
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Improving Generative Adversarial Networks with Denoising Feature Matching
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Unrolled Generative Adversarial Networks
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Generative Multi-Adversarial Networks
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Cooperative Training of Descriptor and Generator Networks
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SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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Improved Techniques for Training GANs
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Machine Learning for Evolution Strategies
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
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LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
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Deep Learning Face Attributes in the Wild
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Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization
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A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization
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Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification
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Huazhong University of Sciences and Technology(HUST), China. He received the B.E. degree in College of
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University of Birmingham Cooperative co-evolutionary module identification with application to cancer disease module discovery
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Learning Multiple Layers of Features from Tiny Images
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Multi-objective optimization using evolutionary algorithms
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His research results have expounded in 20+ publications at prestigious journals and prominent conferences, such as IEEE
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His current research interests span computer vision and machine learning with a series of topics, such as generative modeling and learning