Generative adversarial network

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Information engineering
Machine learning
Artificial neural networks

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Related lectures (27)

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Generative Models: Trajectory Forecasting

Explores generative models for trajectory forecasting in autonomous vehicles, including discriminative vs generative models, VAES, GANS, and case studies.

Deep Generative Models

Covers deep generative models, including LDA, autoencoders, GANs, and DCGANs.

Generative Adversarial Networks: Data Synthesis Techniques

Discusses Generative Adversarial Networks and their applications in synthesizing data and generating images.

Document Analysis and Topic Modeling

Covers document analysis, topic modeling, and deep generative models, including autoencoders and GANs.

Deep Generative Models: Variational Autoencoders & GANs

Explores Variational Autoencoders and Generative Adversarial Networks for deep generative modeling.

Adversarial Machine Learning

Explores adversarial machine learning, generative adversarial networks, and the challenges of adversarial examples in data optimization.

Generative Models: Self-Attention and Transformers

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Evaluating Machine Accuracy and Robustness on ImageNet

Explores the evaluation of machine and human accuracy and robustness on ImageNet, highlighting progress, challenges, and the need for improvement.

Adversarial Machine Learning

Delves into adversarial machine learning, exploring optimization formulations and robustness examples.

Adversarial Machine Learning: Theory and Applications

Covers the theory and applications of adversarial machine learning, focusing on minmax optimization and robustness to adversarial examples.