This paper introduces Provable Unrestricted Adversarial Training (PUAT), a novel adversarial training approach. PUAT enhances robustness against both unrestricted and restricted adversarial examples while improving standard generalizability by aligning the distributions of adversarial examples, natural data, and the classifier's learned distribution. The approach uses partially labeled data and an augmented triple-GAN to generate effective unrestricted adversarial examples, demonstrating superior performance on benchmarks.
Axel Sauer from the University of Tübingen presented research on scaling Generative Adversarial Networks (GANs) using pretrained representations. The work explores shaping GANs into causal structures, training them up to 40 times faster, and achieving state-of-the-art image synthesis. The presentation mentions "Counterfactual Generative Networks", "Projected GANs", "StyleGAN-XL”, and “StyleGAN-T". Why it matters: Scaling GANs and improving their training efficiency is crucial for advancing image and video synthesis, with implications for various applications in computer vision, graphics, and robotics.
The paper introduces OmniGen, a unified framework for generating aligned multimodal sensor data for autonomous driving using a shared Bird's Eye View (BEV) space. It uses a novel generalizable multimodal reconstruction method (UAE) to jointly decode LiDAR and multi-view camera data through volume rendering. The framework incorporates a Diffusion Transformer (DiT) with a ControlNet branch to enable controllable multimodal sensor generation, demonstrating good performance and multimodal consistency.
Nicu Sebe from the University of Trento presented recent work on video generation, focusing on animating objects in a source image using external information like labels, driving videos, or text. He introduced a Learnable Game Engine (LGE) trained from monocular annotated videos, which maintains states of scenes, objects, and agents to render controllable viewpoints. Why it matters: This talk highlights advancements in cross-modal AI, potentially enabling new applications in gaming, simulation, and content creation within the region.