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Found 7 papers, 4 papers with code
Controllable seismic velocity synthesis using generative diffusion models
Accurate seismic velocity estimations are vital to understanding Earth's subsurface structures, assessing natural resources, and evaluating seismic hazards.
A prior regularized full waveform inversion using generative diffusion models
Specifically, we pre-train a diffusion model in a fully unsupervised manner on a prior velocity model distribution that represents our expectations of the subsurface and then adapt it to the seismic observations by incorporating the FWI into the sampling process of the generative diffusion models.
Model-Agnostic Reachability Analysis on Deep Neural Networks
Verification plays an essential role in the formal analysis of safety-critical systems.
Towards Verifying the Geometric Robustness of Large-scale Neural Networks
Deep neural networks (DNNs) are known to be vulnerable to adversarial geometric transformation.
Dynamic Efficient Adversarial Training Guided by Gradient Magnitude
Therefore, based on the magnitude of the gradient, we propose a general acceleration strategy, M+ acceleration, which enables an automatic and highly effective method of adjusting the training procedure.
Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks
Although there are a great number of adversarial attacks on deep learning based classifiers, how to attack object detection systems has been rarely studied.
Generalizing Universal Adversarial Attacks Beyond Additive Perturbations
Extensive experiments are conducted on CIFAR-10 and ImageNet datasets with six deep neural network models including GoogleLeNet, VGG16/19, ResNet101/152, and DenseNet121.
