Search Results for author: Zhichang Guo

Found 9 papers, 2 papers with code

Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability

1 code implementation NeurIPS 2023 Junqi Gao, Biqing Qi, Yao Li, Zhichang Guo, Dong Li, Yuming Xing, Dazhi Zhang

Based on the above facts, we verified that adding perturbations to easy samples in the target class improves targeted adversarial transferability of existing attack methods.

Density Estimation

Diffusion Probabilistic Multi-cue Level Set for Reducing Edge Uncertainty in Pancreas Segmentation

no code implementations11 Apr 2024 Yue Gou, Yuming Xing, Shengzhu Shi, Zhichang Guo

We use the diffusion probabilistic model in the coarse segmentation stage, with the obtained probability distribution serving as both the initial localization and prior cues for the level set method.

Pancreas Segmentation Segmentation

Adversarial Training for Physics-Informed Neural Networks

1 code implementation18 Oct 2023 Yao Li, Shengzhu Shi, Zhichang Guo, Boying Wu

AT-PINNs enhance the robustness of PINNs by fine-tuning the model with adversarial samples, which can accurately identify model failure locations and drive the model to focus on those regions during training.

Adversarial Attack

Re-initialization-free Level Set Method via Molecular Beam Epitaxy Equation Regularization for Image Segmentation

no code implementations13 Oct 2023 Fanghui Song, Jiebao Sun, Shengzhu Shi, Zhichang Guo, Dazhi Zhang

This method uses the crystal growth in the MBE process to limit the evolution of the level set function, and thus can avoid the re-initialization in the evolution process and regulate the smoothness of the segmented curve.

Computational Efficiency Image Segmentation +2

A Review of Adversarial Attacks in Computer Vision

no code implementations15 Aug 2023 Yutong Zhang, Yao Li, Yin Li, Zhichang Guo

Deep neural networks have been widely used in various downstream tasks, especially those safety-critical scenario such as autonomous driving, but deep networks are often threatened by adversarial samples.

Autonomous Driving

Stationary Point Losses for Robust Model

no code implementations19 Feb 2023 Weiwei Gao, Dazhi Zhang, Yao Li, Zhichang Guo, Ovanes Petrosian

CE loss sharpens the neural network at the decision boundary to achieve a lower loss, rather than pushing the boundary to a more robust position.

Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network

no code implementations19 Nov 2022 Xiang Wang, Yimin Yang, Zhichang Guo, Zhili Zhou, Yu Liu, Qixiang Pang, Shan Du

First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain.

Generative Adversarial Network Image Super-Resolution +1

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