Summary of Just Rotate It! Uncertainty Estimation in Closed-source Models Via Multiple Queries, by Konstantinos Pitas et al.
Just rotate it! Uncertainty estimation in closed-source models via multiple queries
by Konstantinos Pitas, Julyan Arbel
First submitted to arxiv on: 22 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 The proposed method estimates uncertainty in closed-source deep neural network image classification models by creating multiple transformed versions of an input image and using them to query the top-1 prediction of the model. This approach demonstrates significant improvements in calibrating uncertainty estimates compared to a naive baseline. The study explores both Gaussian perturbations and natural transformations, finding that the latter yields better-calibrated predictions due to its ability to capture more subtle variations in the input image. A transfer learning approach is also proposed to further improve calibration results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a simple way to predict how certain a deep neural network is about its classification of an image. This method creates multiple versions of an image and uses them to check what the model thinks the most likely answer is. The results show that this approach does better than just assuming the model is always 100% sure or not at all sure. The study looks at two different types of transformations: small changes to the image and more dramatic changes, like rotating it or changing its shape. It finds that the more dramatic changes do a better job of getting accurate uncertainty estimates because they capture more subtle differences in the image. |
Keywords
» Artificial intelligence » Classification » Image classification » Neural network » Transfer learning
