Training-Free Human-in-the-Loop Anomaly Detection via Memory Bank Correction

Ayusha Abbas; Saram Abbas; Kabita Adhikari

cs.LGAug 18, 2026

Training-Free Human-in-the-Loop Anomaly Detection via Memory Bank Correction

Authors: Ayusha Abbas, Saram Abbas, Kabita Adhikari

Organizations: School of Engineering, Newcastle University, Newcastle upon Tyne, UK

Abstract

Anomaly detectors are hardest to deploy exactly where training data is scarcest: a newly commissioned production line has a handful of verified "golden" samples and no machine-learning engineer on the factory floor. We present a training-free human-in-the-loop framework in which a domain expert corrects a PatchCore detector by direct memory bank editing: no retraining, no gradients, no original training data. A false-positive correction inserts the reviewed image's normal patches through a self-calibrating novelty gate admitting only those beyond the median pool-normal nearest-neighbour distance. From a bank built on only ten golden samples, operator corrections close a median 66% of the gap to an uncorrected fully trained bank (mean 80%, raised by three categories that overshoot parity), significantly improving 12 of 15 MVTec AD categories and harming none: ten samples plus corrections outperform hundreds of samples without them. On already-trained banks the headroom is smaller and concentrated where the bank undersamples normal appearance (gated: toothbrush +0.10, metal nut +0.09, zipper +0.05, screw +0.05), and no category except grid is significantly harmed. Evaluation uses a held-out protocol (20 splits per category, Holm-corrected Wilcoxon), because corrected images entering the bank inflate naive evaluation toward AUROC 1.0 by memorisation. Passive and active querying are statistically indistinguishable; a matched-label-budget control attributes gains to deployment-time label production at 43% of exhaustive-review cost; a defect-memory extension fails decisively. Feedback is simulated from ground truth; live expert trials, where mislabelling is costliest on small banks, remain future work.

Figures & tables

Fig. 1: What training-free correction looks like on images the loop never touches. For toothbrush and metal nut, a held-out normal image is falsely flagged before correction (anomaly heatmap hot everywhere) and cleared after a single simulated review session (16–25 single-decision reviews for the seeds shown; the category means are $\sim$ 13 and $\sim$ 20), while a held-out defect remains confidently flagged, with the anomaly map concentrated on the defect itself (contaminated bristles; flipped part). Heatmaps share a colour scale within each row; scores are the image-level anomaly scores.

Method	No re-	Human	Deploy-	Specific
	training	feedback	ment-time	correction
Autoencoder	✗	✗	✗	✗
AnoGAN	✗	✗	✗	✗
PatchCore	✓	✗	✗	✗
Few-shot AD	Partial	✗	Partial	✗
Continual learning	✗	✗	✓	✗

TABLE I: Positioning of our work relative to prior methods.

Panel of Figure 3 — Fig. 2: Framework overview. A deployed PatchCore model flags errors; a quality engineer (not an ML engineer) resolves each with a single binary decision, which is applied as a direct edit to the memory bank and takes effect immediately. The conventional remedy (bottom, dashed) requires an ML team and a retraining pipeline.

Fig. 3: The novelty gate on a real false positive (toothbrush, seed 10). The threshold

\tau_{\text{nov}}

is the median 1-NN distance of all pool-normal patches to the baseline bank (black outline); of the flagged image’s 784 patches, those within existing coverage (left of

\tau

) are rejected and only the genuinely novel ones (right of

\tau

) are inserted.

Category	Type	$\boldsymbol{\sigma}$	Base.	Train	Test (g/a)
Bottle	Object	50.5	1.0000	209	20/63
Leather	Texture	23.1	1.0000	245	32/92
Carpet	Texture	9.5	0.9932	280	28/89
Wood	Texture	7.6	0.9912	247	19/60
Hazelnut	Object	7.0	0.9964	391	40/70
Tile	Texture	6.0	0.9776	230	33/84

TABLE II: All 15 MVTec AD categories, ordered by separation index

\sigma

(descending).

Fig. 4: Anomaly score distributions for four representative categories spanning the full difficulty range (blue = normal, red = anomalous). Overlap decreases as

\sigma

increases, from heavily overlapping (toothbrush) to fully separated (leather).

Category	Type	Sep.( $\sigma$ )	Full-test Base.	HO Base.	Pass. HO Final	Pass. $\Delta$	Act. $\Delta$	Gated $\Delta$
Toothbrush	Object	2.6	0.8139	0.8264	0.9722	$\mathbf{+0.146}^{*}$	$\mathbf{+0.146}$	$\mathbf{+0.101}^{*}$
Grid	Texture	2.6	0.8839	0.8662	0.8190	$-0.047^{*}$	$-0.056$	$-0.031^{*}$
Screw	Object	3.0	0.8703	0.8703	0.9105	$\mathbf{+0.040}^{*}$	$\mathbf{+0.043}$	$\mathbf{+0.048}^{*}$
Transistor	Object	3.7	0.9558	0.9623	0.9379	$-0.024^{*}$	$-0.031$	$+0.005$
Metal Nut	Object	4.3	0.8803	0.9000	0.9980	$\mathbf{+0.098}^{*}$	$\mathbf{+0.098}$	$\mathbf{+0.089}^{*}$
Pill	Object	4.3	0.9242	0.9283	0.9051	$-0.023^{*}$	$-0.026$	$+0.007$

TABLE III: Complete 15-category HITL results under the held-out protocol (means over 20 seeds). HO = held-out set, never corrected. Bold improvements are consistent across seeds (

\geq

15/20 seeds improved); negative improvements are shown as-is; the ungated correction loop is not beneficial on every category. “Gated

\Delta

” is the passive loop with novelty-gated insertion (Section III-B2 ), the recommended deployment configuration. ∗ marks a

\Delta

significantly different from zero (two-sided Wilcoxon signed-rank on per-seed deltas, Holm-corrected across 15 categories,

\alpha=0.05

); it is reported for the passive and gated configurations. Active deltas are not separately tested against zero, because no category shows a significant passive-vs-active difference under the same test.

Category	Cold Base	Post-HITL	Full Ref.	$\boldsymbol{\Delta}$	Recovery	Reviews	Labels to Parity
Bottle	0.957	0.996	1.000	$+0.039^{*}$	0.92	14	5 (7/20)
Cable	0.756	0.866	0.926	$+0.110^{*}$	0.65	50	35 (1/20)
Capsule	0.560	0.699	0.940	$+0.139^{*}$	0.37	80	— (0/20)
Carpet	0.987	0.996	0.993	$+0.008^{*}$	—	18	8 (2/20)
Grid	0.681	0.719	0.866	$+0.038$	0.20	37	24 (6/20)
Hazelnut	0.921	0.998	0.996	$+0.077^{*}$	1.03	16	4 (14/20)

TABLE IV: Cold-start results: initial bank built from only

k=10

training normals (78 patches), passive querying with novelty-gated insertion, 90-round budget, means over 20 seeds. “Full Ref.” = full-training-bank held-out baseline on the same seed (paired). Recovery = fraction of the cold-to-full gap closed (shown where the gap exceeds 0.02). “Labels to parity” = median reviews to reach the full-training reference, with the number of seeds reaching it. ∗ = Holm-significant

\Delta

(Wilcoxon,

\alpha=0.05

); no category is significantly negative.

Fig. 5: Held-out trajectories (mean

\pm 1

std over 20 seeds). (a–c) Cold-start recovery from 10 golden samples with the novelty gate: metal nut crosses its full-training baseline around round 50 and ends above it; zipper converges to just below parity after a transient early dip (corrections on a 78-patch bank briefly disturb scores before coverage accumulates); screw is still climbing when the 90-round budget ends, far from parity (the budget-limited case). (d) Pill on a fully trained bank: the ungated loop drifts below baseline while the novelty-gated loop stays above it, showing the gate’s effect in a single panel.

Fig. 6: Held-out ROC curves before (grey) and after correction under passive (blue) and active (red dashed) querying, for the three largest-improvement categories at their median-improvement seed. The corrected ROC dominates the baseline everywhere; passive and active endpoints are nearly identical.

Fig. 7: Held-out AUROC over correction rounds for passive (blue) and active (red) querying, on six categories spanning the difficulty range (mean over 20 seeds; bands are the 95% confidence interval of the mean,

t

-based with

n=20

, not the seed-to-seed spread). The bands overlap at every round except on metal nut, where active querying rises faster over the first few rounds before both strategies converge to the same endpoint: a difference in speed, not destination.

Fig. 8: Per-seed distributions of held-out AUROC improvement (20 seeds per category; blue = passive, red = active). Toothbrush, metal nut, zipper and screw improve consistently; grid, transistor, pill, capsule, cable and tile centre at or below zero. Passive and active distributions overlap heavily on every category.

Mode	HO Final	$\boldsymbol{\Delta}$	HO AUCC	FP/FN corr.
FP-only	0.9722	$+0.146$	0.709	7.5/0
Both	0.9722	$+0.146$	0.563	7.6/5.0
FN-only	0.8139	$-0.013$	0.004	0/8.8

TABLE V: FP vs. FN correction ablation, toothbrush passive, held-out protocol (means over 20 seeds). Restricted modes exclude the skipped error type from review;

\Delta

is held-out final minus baseline (0.826).

Fig. 9: FP vs. FN correction mix across all 15 categories (mean counts over 20 seeds). Blue = FP, red = FN. Bottle and leather are ceiling controls.

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