标题： 基于自适应邻域和辅助正则化的不平衡鲁棒且采样高效的连续条件GANs 显示英文标题

标题： Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinity and Auxiliary Regularization

摘要： 近年来，条件生成建模的进展引入了连续条件生成对抗网络（CcGAN）和连续条件扩散模型（CCDM），用于估计在标量、连续回归标签（例如角度、年龄或温度）条件下的高维数据分布。然而，这些方法面临根本性的限制：CcGAN由于固定大小的邻域约束而受到数据不平衡的影响，而CCDM需要计算成本高昂的迭代采样。我们提出了CcGAN-AVAR，这是一种增强的CcGAN框架，能够解决这两个挑战：(1) 利用GAN框架的原生一步生成来克服CCDM的采样瓶颈（实现300倍至2000倍的推理速度提升），同时(2) 两个新的组件专门针对数据不平衡——一种自适应邻域机制，可以动态调整邻域的大小，以及一个多任务判别器，通过辅助回归和密度比估计构建两个正则化项，显著提高生成器训练。在四个基准数据集（64x64到192x192分辨率）上的大量实验，跨越八个具有挑战性的不平衡设置，证明CcGAN-AVAR在保持采样效率的同时实现了最先进的生成质量。 显示英文摘要

摘要： Recent advances in conditional generative modeling have introduced Continuous conditional Generative Adversarial Network (CcGAN) and Continuous Conditional Diffusion Model (CCDM) for estimating high-dimensional data distributions conditioned on scalar, continuous regression labels (e.g., angles, ages, or temperatures). However, these approaches face fundamental limitations: CcGAN suffers from data imbalance due to fixed-size vicinity constraints, while CCDM requires computationally expensive iterative sampling. We present CcGAN-AVAR, an enhanced CcGAN framework that addresses both challenges: (1) leveraging the GAN framework's native one-step generation to overcome CCDMs' sampling bottleneck (achieving 300x-2000x faster inference), while (2) two novel components specifically target data imbalance - an adaptive vicinity mechanism that dynamically adjusts vicinity's size, and a multi-task discriminator that constructs two regularization terms (through auxiliary regression and density ratio estimation) to significantly improve generator training. Extensive experiments on four benchmark datasets (64x64 to 192x192 resolution) across eight challenging imbalanced settings demonstrate that CcGAN-AVAR achieves state-of-the-art generation quality while maintaining sampling efficiency.