本文整理了2019年CVPR、ECCV、ICLR等会议上的生成对抗网络（GAN）相关论文，涉及高保真3D人脸重建、多样图像合成、反馈对抗学习、结构化CNN修剪、风格化生成对抗网络等多个方向，同时涵盖了风格迁移领域的最新成果，如注意力机制、多笔画风格转移和快速图像风格转换等。
CVPR2019(只整理了可能与通用模型改进相关的):
GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
Feedback Adversarial Learning: Spatial Feedback for Improving Generative Adversarial Networks
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning
Sliced Wasserstein Generative Models
Balanced Self-Paced Learning for Generative Adversarial Clustering Network
A Style-Based Generator Architecture for Generative Adversarial Networks
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