microbatchGAN: Stimulating Diversity with Multi-Adversarial Discrimination

基于掩码判别（Masked Discrimination）的少样本图像生成

Few-shot Image Generation via Masked Discrimination

针对少样本GAN易过拟合、生成器复制训练样本的问题，本文提出掩码判别与跨域一致性损失，提升适配后生成图像的质量与多样性。

Self-Adversarial Learning with Comparative Discrimination for Text Generation

Conventional Generative Adversarial Networks (GANs) for text generation tend to have issues of reward sparsity and mode collapse that affect the quality and diversity of generated samples. To address the issues, we propose a novel self-adversarial learning (SAL) paradigm for improving GANs' performance in text generation. In contrast to standard GANs that use a binary classifier as its discriminator to predict whether a sample is real or generated, SAL employs a comparative discriminator which is a pairwise classifier for comparing the text quality between a pair of samples. During training, S…

MGD-GAN: Text-to-Pedestrian generation through Multi-Grained Discrimination

In this paper, we investigate the problem of text-to-pedestrian synthesis, which has many potential applications in art, design, and video surveillance. Existing methods for text-to-bird/flower synthesis are still far from solving this fine-grained image generation problem, due to the complex structure and heterogeneous appearance that the pedestrians naturally take on. To this end, we propose the Multi-Grained Discrimination enhanced Generative Adversarial Network, that capitalizes a human-part-based Discriminator (HPD) and a self-cross-attended (SCA) global Discriminator in order to capture …

CircleGAN: Generative Adversarial Learning across Spherical Circles

We present a novel discriminator for GANs that improves realness and diversity of generated samples by learning a structured hypersphere embedding space using spherical circles. The proposed discriminator learns to populate realistic samples around the longest spherical circle, i.e., a great circle, while pushing unrealistic samples toward the poles perpendicular to the great circle. Since longer circles occupy larger area on the hypersphere, they encourage more diversity in representation learning, and vice versa. Discriminating samples based on their corresponding spherical circles can thus …

通过结构判别与纹理调制改进少样本图像生成

Improving Few-shot Image Generation by Structural Discrimination and Textural Modulation

本文提出纹理调制与全局结构判别机制，实现细粒度语义注入和布局引导，在少样本图像生成中达到最先进性能，且可无缝增强现有模型。

CDE-GAN: Cooperative Dual Evolution Based Generative Adversarial Network

Generative adversarial networks (GANs) have been a popular deep generative model for real-world applications. Despite many recent efforts on GANs that have been contributed, mode collapse and instability of GANs are still open problems caused by their adversarial optimization difficulties. In this paper, motivated by the cooperative co-evolutionary algorithm, we propose a Cooperative Dual Evolution based Generative Adversarial Network (CDE-GAN) to circumvent these drawbacks. In essence, CDE-GAN incorporates dual evolution with respect to the generator(s) and discriminators into a unified evolu…