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Found 7 papers, 0 papers with code
Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning
With the class-wise robust features, the model explicitly learns adversarially robust features through the proposed robust proxy learning framework.
Advancing Adversarial Training by Injecting Booster Signal
Different from the previous approaches, in this paper, we propose a new approach to improve the adversarial robustness by using an external signal rather than model parameters.
Defending Person Detection Against Adversarial Patch Attack by using Universal Defensive Frame
Person detection has attracted great attention in the computer vision area and is an imperative element in human-centric computer vision.
Structure Boundary Preserving Segmentation for Medical Image With Ambiguous Boundary
We demonstrate that the proposed method could surpass the state-of-the-art segmentation network and improve the accuracy of three different segmentation network models on different types of medical image datasets.
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning
The success of multimodal data fusion in deep learning appears to be attributed to the use of complementary in-formation between multiple input data.
Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation
Experimental results show that the proposed method could provide useful uncertainty information by Bayesian approximation with the efficient ensemble model generation and improve the predictive performance.
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack
After the ensemble models are trained, it can hide the gradient efficiently and avoid the gradient-based attack by the random layer sampling method.
