Rethinking Multidimensional Discriminator Output for Generative Adversarial Networks

Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks

In this paper, we examine the long-neglected yet important effects of point sampling patterns in point cloud GANs. Through extensive experiments, we show that sampling-insensitive discriminators (e.g.PointNet-Max) produce shape point clouds with point clustering artifacts while sampling-oversensitive discriminators (e.g.PointNet++, DGCNN) fail to guide valid shape generation. We propose the concept of sampling spectrum to depict the different sampling sensitivities of discriminators. We further study how different evaluation metrics weigh the sampling pattern against the geometry and propose s…

MonoFlow：从Wasserstein梯度流视角重新思考散度GAN

MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows

本文提出统一生成建模框架MonoFlow，利用Wasserstein梯度流重新解释GAN对抗训练，揭示变分散度最小化与对抗训练的本质区别，并指出只要实现MonoFlow，GAN可拥有更多损失设计。

EpiGRAF: Rethinking training of 3D GANs

A very recent trend in generative modeling is building 3D-aware generators from 2D image collections. To induce the 3D bias, such models typically rely on volumetric rendering, which is expensive to employ at high resolutions. During the past months, there appeared more than 10 works that address this scaling issue by training a separate 2D decoder to upsample a low-resolution image (or a feature tensor) produced from a pure 3D generator. But this solution comes at a cost: not only does it break multi-view consistency (i.e. shape and texture change when the camera moves), but it also learns th…

基于不变统计损失的多元重尾分布隐式生成模型鲁棒训练

Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical loss

针对传统隐式生成模型训练不稳定、难捕捉重尾分布特性及高维拓展困难的问题，研究提出基于随机投影和广义帕累托噪声的Pareto-ISL方法，可鲁棒建模多元重尾分布，还可作为GAN预训练技术缓解模式坍缩。