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Found 6 papers, 3 papers with code
MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA based Mixture of Experts
Unlike other LoRA based MoE methods, MixLoRA enhances model performance by utilizing independently configurable attention-layer LoRA adapters, supporting the use of LoRA and its variants for the construction of experts, and applying auxiliary load balance loss to address the imbalance problem of the router.
BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks
Our attack prototype, named BadPart, is evaluated on both MDE and OFE tasks, utilizing a total of 7 models.
Fusion is Not Enough: Single Modal Attacks on Fusion Models for 3D Object Detection
We argue that the weakest link of fusion models depends on their most vulnerable modality, and propose an attack framework that targets advanced camera-LiDAR fusion-based 3D object detection models through camera-only adversarial attacks.
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks
We improve adversarial robustness against physical-world attacks using L0-norm-bounded perturbation in training.
Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches
Experimental results show that our method can generate stealthy, effective, and robust adversarial patches for different target objects and models and achieves more than 6 meters mean depth estimation error and 93% attack success rate (ASR) in object detection with a patch of 1/9 of the vehicle's rear area.
Music Style Classification with Compared Methods in XGB and BPNN
Scientists have used many different classification methods to solve the problem of music classification.
