Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization

2026-07-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

CcGAN-AVAR is an enhanced Continuous conditional Generative Adversarial Network (CcGAN) framework designed to overcome data imbalance and slow sampling in continuous conditional generative modeling. It achieves 300×-2000× faster inference compared to Continuous Conditional Diffusion Models (CCDM) by leveraging GAN's one-step generation. The framework introduces an adaptive vicinity mechanism that dynamically adjusts vicinity size and a multi-task discriminator with auxiliary regression and density ratio estimation for improved generator training. Extensive experiments on four benchmark datasets (64×64 to 192×192 resolution) across eight challenging imbalanced settings demonstrate CcGAN-AVAR's superior generation quality and sampling efficiency. It also includes an optimized codebase and a new imbalanced variant of the RC-49 dataset, RC-49-I.

Key takeaway

For machine learning engineers developing conditional image generation systems, CcGAN-AVAR offers a robust solution for imbalanced datasets without sacrificing inference speed. You should consider integrating its adaptive vicinity and multi-task discriminator components to enhance generation quality and label consistency, especially when working with continuous regression labels. This approach provides a significant speed advantage over diffusion models, making it suitable for real-time or high-throughput applications.

Key insights

CcGAN-AVAR improves conditional image generation by adaptively handling data imbalance and accelerating sampling.

Principles

• Dynamic vicinity adjustment enhances label consistency.
• Multi-task discriminators improve generator training.
• GANs offer faster sampling than diffusion models.

Method

CcGAN-AVAR uses a soft/hybrid adaptive vicinity and a multi-task discriminator with auxiliary regression and density ratio estimation branches to regularize generator training.

In practice

• Implement adaptive vicinity for imbalanced regression labels.
• Integrate auxiliary regression and DRE into GAN discriminators.
• Consider GANs for high-speed conditional generation.

Topics

• Continuous Conditional GANs
• Data Imbalance
• Adaptive Vicinity
• Generative Adversarial Networks
• Diffusion Models
• Image Generation
• Auxiliary Regularization

Code references

• UBCDingXin/CcGAN-AVAR
• SullyChen/driving-datasets

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.