Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Anomaly detection is a critical aspect of ensuring product quality and minimising defects in manufacturing processes. The MVTec anomaly detection (MVTec AD) dataset is a well-known benchmark for evaluating the effectiveness of anomaly detection methods in real-world scenarios. In this paper, we present a novel approach to anomaly detection on the MVTec AD dataset by leveraging upon the two-level vector quantised variational autoencoder (VQ-VAE-2) architecture. It encodes defect-free images onto a discrete latent space, and a powerful PixelSnail prior is fitted over the discrete latent space induced by the data. Latent codes with a cross-entropy loss above a certain threshold are assumed to correspond to anomalies. The threshold is usually manually tuned and fixed across the various object and texture categories of the dataset. This is time consuming and suboptimal as a different threshold may be required for each category. We introduce an automatic way of determining this threshold: since the cross-entropy losses follow a log-normal distribution where the distribution for defect-free images lies within the distribution for defect images, we found that a threshold corresponding to half of the maximum loss for defect-free images works well. During inference, the PixelSnail prior is repeatedly called as each pixel is conditioned on the previous pixels in a raster scan order (left to right, top to bottom) which is computationally expensive. We found that the model can be called once for each row of the latent map achieving an order-of-magnitude speedup without a significant drop in performance. Lastly, we show that there is a statistical improvement over the original VQ-VAE and performance is similar to the state-of-the-art.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it