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Found 4 papers, 3 papers with code
Leveraging Latent Features for Local Explanations
As the application of deep neural networks proliferates in numerous areas such as medical imaging, video surveillance, and self driving cars, the need for explaining the decisions of these models has become a hot research topic, both at the global and local level.
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks
Recent studies have shown that adversarial examples in state-of-the-art image classifiers trained by deep neural networks (DNN) can be easily generated when the target model is transparent to an attacker, known as the white-box setting.
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
important object pixels in an image) to justify its classification and analogously what should be minimally and necessarily \emph{absent} (viz.
FEAST: An Automated Feature Selection Framework for Compilation Tasks
In this paper, we propose FEAture Selection for compilation Tasks (FEAST), an efficient and automated framework for determining the most relevant and representative features from a feature pool.
