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Found 5 papers, 5 papers with code
Repairing Group-Level Errors for DNNs Using Weighted Regularization
In particular, it can repair confusion error and bias error of DNN models for both single-label and multi-label image classifications.
Understanding Local Robustness of Deep Neural Networks under Natural Variations
To this end, we study the local per-input robustness properties of the DNNs and leverage those properties to build a white-box (DeepRobust-W) and a black-box (DeepRobust-B) tool to automatically identify the non-robust points.
Code Prediction by Feeding Trees to Transformers
We provide comprehensive experimental evaluation of our proposal, along with alternative design choices, on a standard Python dataset, as well as on a Python corpus internal to Facebook.
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Testing DNN Image Classifiers for Confusion & Bias Errors
We found that many of the reported erroneous cases in popular DNN image classifiers occur because the trained models confuse one class with another or show biases towards some classes over others.
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars
Most existing testing techniques for DNN-driven vehicles are heavily dependent on the manual collection of test data under different driving conditions which become prohibitively expensive as the number of test conditions increases.
