Summary of Error-driven Uncertainty Aware Training, by Pedro Mendes and Paolo Romano and David Garlan
Error-Driven Uncertainty Aware Training
by Pedro Mendes, Paolo Romano, David Garlan
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 In this research paper, a novel technique called Error-Driven Uncertainty Aware Training (EUAT) is presented to improve neural networks’ uncertainty estimation capabilities. The EUAT approach aims to make models more uncertain when they produce inaccurate predictions and less uncertain when their outputs are accurate. This is achieved by selectively employing two loss functions during the training phase: one for correctly predicted inputs and another for mispredicted inputs. The goal is to minimize model uncertainty for correct predictions and maximize uncertainty for incorrect predictions, while maintaining the model’s misprediction rate. To evaluate EUAT, various neural models and datasets are used in image recognition domains under both non-adversarial and adversarial settings. Results show that EUAT outperforms existing approaches for uncertainty estimation, providing high-quality uncertainty estimates when evaluated via statistical metrics and when employed to build binary classifiers that determine whether the model’s output can be trusted. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EUAT is a new way to train neural networks to estimate their uncertainty correctly. This is important because neural networks often think they’re right even when they’re wrong, which makes it hard to trust them. EUAT helps neural networks be more uncertain when they make mistakes and less uncertain when they get things right. To do this, EUAT uses two different ways of measuring how well the model is doing during training: one for when the model gets something right and another for when it gets something wrong. This approach has been tested on many different kinds of neural networks and datasets, and it performs better than other methods at estimating uncertainty. |
