Summary of Counterfactual Gradients-based Quantification Of Prediction Trust in Neural Networks, by Mohit Prabhushankar and Ghassan Alregib
Counterfactual Gradients-based Quantification of Prediction Trust in Neural Networks
by Mohit Prabhushankar, Ghassan AlRegib
First submitted to arxiv on: 22 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes GradTrust, a method for evaluating the trustworthiness of deep neural network predictions. The approach uses variance in counterfactual gradients to quantify the uncertainty associated with each prediction. Experimental results on large-scale image and video recognition tasks demonstrate that GradTrust outperforms existing methods for detecting misprediction rates. The study also shows that simple classification techniques like negative log likelihood and margin classifiers can be more effective than state-of-the-art uncertainty estimation methods. The proposed method is evaluated on a range of architectures, including those pre-trained on ImageNet and Kinetics-400 datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to measure how trustworthy deep learning models are when they make predictions. It uses a special kind of gradient that shows how the model would change if it made a different prediction. The authors tested this method on big image and video recognition tasks and found that it works better than other methods for detecting when the model is wrong. They also showed that simple classification techniques can be more effective than fancy uncertainty estimation methods. |
Keywords
» Artificial intelligence » Classification » Deep learning » Log likelihood » Neural network
