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Found 3 papers, 2 papers with code
Conditional Generative Models for Counterfactual Explanations
Counterfactual instances offer human-interpretable insight into the local behaviour of machine learning models.
Monitoring and explainability of models in production
The machine learning lifecycle extends beyond the deployment stage.
Adversarial Detection and Correction by Matching Prediction Distributions
We present a novel adversarial detection and correction method for machine learning classifiers. The detector consists of an autoencoder trained with a custom loss function based on the Kullback-Leibler divergence between the classifier predictions on the original and reconstructed instances. The method is unsupervised, easy to train and does not require any knowledge about the underlying attack.
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