Summary of Onboard Out-of-calibration Detection Of Deep Learning Models Using Conformal Prediction, by Protim Bhattacharjee and Peter Jung
Onboard Out-of-Calibration Detection of Deep Learning Models using Conformal Prediction
by Protim Bhattacharjee, Peter Jung
First submitted to arxiv on: 4 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper explores the application of conformal prediction in remote sensing, a critical domain where deep learning models’ black box nature can be detrimental. Conformal prediction provides finite sample coverage guarantees, ensuring trust in model outputs by offering a prediction set that contains the true class within a user-defined error rate, given data exchangeability. The paper shows that conformal prediction algorithms are related to the uncertainty of deep learning models and can detect when they become out-of-calibration. Popular classification models like Resnet50, Densenet161, InceptionV3, and MobileNetV2 are applied on remote sensing datasets such as EuroSAT to demonstrate the model’s outputs becoming untrustworthy under noisy scenarios. The authors also present an out-of-calibration detection procedure that relates model uncertainty to the average size of conformal prediction sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making sure we can trust computers to make decisions in important areas like monitoring the environment from space. Right now, computers use deep learning models to do this job, but they’re like black boxes that don’t explain how they made their decisions. Conformal prediction is a way to fix this problem by providing guarantees that the computer’s decision will be correct within a certain margin of error. The paper shows how conformal prediction can help detect when computers are making mistakes and provide a solution to improve their performance. |
Keywords
» Artificial intelligence » Classification » Deep learning
