How Image Corruption and Perturbation Affect Out-of-Distribution Generalization and Calibration
Bibliographic record
Abstract
In scenarios such as people-flow analysis and autonomous driving using camera footage, the performance of deep neural networks can be impacted by environmental changes such as the degradation of the camera lenses over time and changes in weather conditions. Understanding the impact of environmental changes on model performance is critical to the safe use of the model but has not been systematically investigated. In this study, we cast environmental changes as distribution shifts in the data and tackle the out-of-distribution generalization problem. In particular, we investigate the impact of performance degradation and uncertainty estimation under more than 30 distribution shifts considered in real-world applications. Our experimental results show that there is a correlation between classification accuracy and uncertainty in out-of-distribution environments. Furthermore, it is suggested that uncertainty calibration is not necessarily effective in environments where severe corruption occurs. We believe these findings will contribute to the safe use of the model in the real world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".