Fine-tuning the neural network on many adversarial examples will make it more robust against adversarial attacks. Adversarial training results in a slight drop in the accuracy of a deep learning ...

Adversarial examples do not just apply to neural networks that process visual data. There is also research on adversarial machine learning on text and audio data.

Generative adversarial networks, or GANs, are deep learning frameworks for unsupervised learning that utilize two neural networks. The two networks are pitted against each other, with one generating ...

For example, adversarial images generated using the PGD method (perturbation = 0.002) resulted in a DL model ... Minagi A, Takemoto K: Universal adversarial attacks on deep neural networks for medical ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. ... Adversarial networks.

Previous adversarial examples have largely been designed in “white box” settings, where computer scientists have access to the underlying mechanics that power an algorithm. In these scenarios ...

It is going to apply generative adversarial networks to holographic image generation. GAN is a neural network model consisting of a generator and ...

Case in point: researchers at NVIDIA have harnessed the power of a generative adversarial network (GAN) — a class of neural network — to generate some extremely realistic faces.