Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples

18 Aug 2017 • Yinpeng Dong • Hang Su • Jun Zhu • Fan Bao

Deep neural networks (DNNs) have demonstrated impressive performance on a wide array of tasks, but they are usually considered opaque since internal structure and learned parameters are not interpretable. In this paper, we re-examine the internal representations of DNNs using adversarial images, which are generated by an ensemble-optimization algorithm... (read more)

PDF Abstract

Code

Add Remove Mark official

No code implementations yet. Submit your code now

Tasks

Add Remove

Results from the Paper

Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

METHOD	TYPE
Interpretability	Image Models

METHOD

TYPE

Interpretability

Image Models

Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples

Code

Add Remove Mark official

Tasks

Add Remove

Datasets

Results from the Paper

Edit

Methods used in the Paper

Add Remove

Edit Social Preview

Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples Edit social preview

Code Edit Add Remove Mark official

Tasks Edit Add Remove

Datasets Edit

Results from the Paper Edit

Methods used in the Paper Edit Add Remove

Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples

Code

Add Remove Mark official

Tasks

Add Remove

Datasets

Results from the Paper

Edit

Methods used in the Paper

Add Remove