PHD Gans for outlier Detection

用于异常值检测的 PHD Gans

• 批准号: 2131028
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2131028
• 关键词: PHD; Gans; outlier; Detection

The project aims at researching a novel anomaly detection model, namely it will address the problem of determining normal from abnormal observations when the dataset in hand is highly biased towards the normal class. This will be achieved by using modern deep learning frameworks (generative adversarial networks) in combination with traditional anomaly detection algorithms such as one-class support vector machines. Applications will include the reference benchmark MNIST and CIFAR image datasets, along with more complex brain imaging data related to different mental health conditions.

该项目旨在研究一种新的异常检测模型，即当手头的数据集高度偏向正常类时，它将解决从异常观测中确定正常的问题。这将通过使用现代深度学习框架（生成对抗网络）与传统的异常检测算法（如一类支持向量机）相结合来实现。应用程序将包括参考基准MNIST和CIFAR图像数据集，沿着与不同心理健康状况相关的更复杂的大脑成像数据。