Summary of The Bayesian Confidence (bacon) Estimator For Deep Neural Networks, by Patrick D. Kee et al.
The Bayesian Confidence (BACON) Estimator for Deep Neural Networks
by Patrick D. Kee, Max J. Brown, Jonathan C. Rice, Christian A. Howell
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 introduces a new method called Bayesian Confidence Estimator (BACON) to interpret deep neural networks’ output layers. The traditional approach of interpreting Softmax values as probabilities is flawed, leading to extreme predictions. BACON builds upon Waagen’s geometric model, using Bayes’ Rule and validation data to estimate likelihood and normalization values. This method shows superior calibration error compared to Softmax for ResNet-18 at 85% accuracy and EfficientNet-B0 at 95% accuracy on the CIFAR-10 dataset with an imbalanced test set. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem in deep learning by creating a new way to understand what neural networks predict. Right now, people think that Softmax values mean something they don’t. The researchers created BACON, which uses Bayes’ Rule and some data to make better predictions. It works well for certain kinds of networks and datasets. |
Keywords
» Artificial intelligence » Deep learning » Likelihood » Resnet » Softmax
