MonoFlow：从Wasserstein梯度流视角重新思考散度GAN

Rethinking Exposure Bias In Language Modeling

Exposure bias describes the phenomenon that a language model trained under the teacher forcing schema may perform poorly at the inference stage when its predictions are conditioned on its previous predictions unseen from the training corpus. Recently, several generative adversarial networks (GANs) and reinforcement learning (RL) methods have been introduced to alleviate this problem. Nonetheless, a common issue in RL and GANs training is the sparsity of reward signals. In this paper, we adopt two simple strategies, multi-range reinforcing, and multi-entropy sampling, to amplify and denoise the…

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…

Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs

Convolutional Neural Networks (CNN)-based approaches have shown promising results in pansharpening of satellite images in recent years. However, they still exhibit limitations in producing high-quality pansharpening outputs. To that end, we propose a new self-supervised learning framework, where we treat pansharpening as a colorization problem, which brings an entirely novel perspective and solution to the problem compared to existing methods that base their solution solely on producing a super-resolution version of the multispectral image. Whereas CNN-based methods provide a reduced resolutio…

复杂性至关重要：重新思考生成建模的隐空间

Complexity Matters: Rethinking the Latent Space for Generative Modeling

本文从模型复杂性视角重新思考生成建模的隐空间，提出隐-数据分布距离及解耦自编码器（DAE）两阶段训练策略，在 VQGAN 和 Diffusion Transformer 等模型上以更低模型复杂性显著提升生成质量。

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…

重新思考生成对抗网络的编辑：一种基于降维的编辑向量估计方法

Rethinking the editing of generative adversarial networks: a method to estimate editing vectors based on dimension reduction

本文提出一种不依赖语义分割和可微网络的GAN编辑向量估计方法，通过改进LDA处理二元和连续特征编辑，在服装、肤色等非语义特征上表现良好。