generative-adversarial-networks-56260f09·2 events·first seen Aliases: Generative Adversarial Networks, Generative Adversarial Network
A new arXiv paper investigates whether data augmentation can neutralize poisoning attacks on 3D point cloud datasets used in connected and autonomous vehicle (CAV) perception systems. The authors find that poisoning survives GAN-based augmentation pipelines, propagates into augmented datasets, and continues to degrade downstream classifier decisions. Experimental materials including tools, datasets, and classifiers are released publicly to support reproducibility.
OpenAI published research on stable and scalable training of energy-based models (EBMs), achieving sample quality competitive with GANs at low temperatures while retaining mode coverage guarantees of likelihood-based models. The approach uses iterative compute during generation to continually refine outputs. This work positions EBMs as a promising alternative generative modeling paradigm bridging GANs and likelihood-based models.