Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization
arXiv:2508.01725v5 Announce Type: replace Abstract: Recent advances in continuous conditional generative modeling, including Continuous conditional Generative Adversarial Network (CcGAN) and Continuous Conditional Diffusion Model (CCDM), estimate high-dimensional data distributions conditioned on scalar regression labels such as angles, ages, or temperatures. However, fixed-size vicinal training in CcGAN can be sensitive to non-uniform label densities, whereas CCDM relies on computationally expensive iterative sampling. To address these issues, we propose CcGAN-AVAR, an imbalance-aware extensi
The paper addresses current challenges in generative AI, specifically the computational expense of diffusion models and sensitivity of GANs to label density, indicating an active research front for improving practical applications.
Advancements in continuous conditional GANs can lead to more efficient and robust generation of complex datasets, which is crucial for various AI applications requiring high-quality synthetic data for training.
This research introduces a more imbalance-robust and sampling-efficient approach for continuous conditional generative modeling, potentially reducing computational costs and improving data fidelity in areas like image synthesis and data augmentation.
- · AI researchers
- · Data scientists
- · Companies using generative AI for data synthesis
- · Gaming and media industries
- · Inefficient generative model architectures
- · Projects constrained by high computational costs for data generation
More accurate and diverse synthetic datasets become available for training advanced AI models.
Reduced data acquisition and annotation costs as synthetic data quality improves to a 'fit-for-purpose' level.
Acceleration of AI development in fields where real-world data is scarce, sensitive, or expensive to obtain.
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