Reconstruction-based Out-of-Distribution Detection for Short-Range FMCW Radar
Bibliographic record
Abstract
Out-of-distribution (OOD) detection recently has drawn attention due to its critical role in the safe deployment of modern neural network architectures in real-world applications. The OOD detectors aim to distinguish samples that lie outside the training distribution in order to avoid the overconfident predictions of machine learning models on OOD data. Existing detectors, which mainly rely on the logit, intermediate feature space, softmax score, or reconstruction loss, manage to produce promising results. However, most of these methods are developed for the image domain. In this study, we propose a novel reconstruction-based OOD detector to operate on the radar domain. Our method exploits an autoencoder (AE) and its latent representation to detect the OOD samples. We propose two scores incorporating the patch-based reconstruction loss and the energy value calculated from the latent representations of each patch. We achieve an AUROC of 90.72% on our dataset collected by using 60 GHz short-range FMCW Radar. The experiments demonstrate that, in terms of AUROC and AUPR, our method outperforms the baseline (AE) and the other state-of-the-art methods. Also, thanks to its model size of 641 kB, our detector is suitable for embedded usage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".