Memory-Bounded Continuation of Greedy Sampling for Continual Anomaly Detection

Yoon Gyo Jung; Jaewoo Park; Kuan-Chuan Peng; Seongdeok Bang; Octavia Camps

cs.CVAug 15, 2026

Memory-Bounded Continuation of Greedy Sampling for Continual Anomaly Detection

Authors: Yoon Gyo Jung, Jaewoo Park, Kuan-Chuan Peng, Seongdeok Bang, Octavia Camps

Organizations: Electrical and Computer Engineering Department Northeastern University Boston, MA, USA · AIVEX Inc. Seoul, South Korea · Mitsubishi Electric Research Laboratories (MERL) Cambridge, MA, USA

Abstract

Greedy sampling produces a compact yet representative summary of normal data, which is essential for reliable anomaly detection that relies on measuring distance from normality. For continual anomaly detection where tasks arrive sequentially, extending greedy sampling is straightforward with unbounded memory through coreset accumulation. However, practical deployment requires fixed memory where the coreset size remains constant regardless of task count. We observe that continued greedy sampling, which iteratively applies greedy selection over previously greedy-sampled sets, effectively preserves representativeness under strict memory limits. Despite discarding data at each step to satisfy the memory constraint, coreset quality degrades gracefully rather than catastrophically, enabling reliable anomaly detection across the tasks. We provide theoretical justification by showing that resulting greedy-continued coreset approximates the oracle coreset within a bounded gap. We instantiate this principle in ContCore, which constructs a greedy-continued coreset through greedy expansion on new task features followed by greedy consolidation to enforce the memory budget. Unlike neural methods susceptible to catastrophic forgetting or naive coreset accumulation requiring unbounded memory, ContCore maintains fixed memory with theoretical guarantees. Empirically, ContCore achieves state-of-the-art performance across 11 task schedules on MVTecAD and VisA, and extends effectively to online continual AD settings where prior methods degrade significantly. Code: https://github.com/jungyg/ContCore

Figures & tables

Figure 1: (a) Comparison of task average image-AUROC for CAD baselines and ContCore on

1\times 15

and

10-1\times 5

task schedules on MVTecAD, which demonstrates the effect of catastrophic forgetting in continual learning. (b) Efficiency comparison of CAD methods.

Figure 2: Memory-bounded continuation of greedy sampling via ContCore. Greedy expansion selects maximally distant features from new task features relative to the current coreset (green circles). Greedy consolidation enforces the memory constraint by selecting maximally separated features from the combined set (red circles), producing a greedy-continued coreset that preserves coverage across tasks.

Method	1 × 15 with 15 Steps		3 × 5 with 5 Steps		10 – 1 × 5 with 6 Steps		10 – 5 with 2 Steps		14 – 1 with 2 Steps
Method	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )
UniAD $\;{}_{\text{NeurIPS'22}}$ ?	77.4 / -	22.9 / -	81.3 / 88.7	7.4 / 10.6	76.6 / 82.3	21.1 / 17.3	86.7 / 91.5	14.9 / 10.6	85.7 / 89.6	18.3 / 13.3
UniAD + EWC $\;{}_{\text{PNAS'17}}$ ?	- / -	- / -	79.6 / 89.0	9.5 / 10.1	89.6 / 93.8	5.4 / 3.6	90.5 / 93.6	7.3 / 4.2	92.8 / 95.4	4.1 / 1.9
UniAD + SI $\;{}_{\text{PMLR'17}}$ ?	- / -	- / -	81.9 / 88.5	7.0 / 10.8	77.2 / 81.6	20.2 / 18.2	84.1 / 88.3	20.2 / 17.0	85.7 / 89.5	18.4 / 13.4
UniAD + MAS $\;{}_{\text{ECCV'18}}$ ?	- / -	- / -	81.5 / 89.0	7.2 / 10.2	77.9 / 82.0	19.5 / 17.7	86.8 / 91.0	14.9 / 11.6	85.8 / 89.6	18.1 / 13.3
UniAD + LVT $\;{}_{\text{CVPR'22}}$ ?	- / -	- / -	80.4 / 88.6	8.6 / 10.6	78.2 / 88.3	19.1 / 16.1	87.1 / 90.6	14.1 / 12.3	80.4 / 86.0	29.1 / 20.6

Table 1: Task average AUROC (

\uparrow

) and forgetting measurement (FM) (

\downarrow

) on MVTecAD dataset with 5 different task schedules. Each cell shows the image-level/pixel-level AUROC. The best and second best results are marked in bold and underlined , respectively.

Method	1 × 12 with 12 Steps		8 – 1 × 4 with 5 Stepss		8 – 4 with 2 Steps		11 – 1 with 2 Steps
Method	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )
UniAD $\;{}_{\text{NeurIPS'22}}$ ?	63.9 / -	29.7 / -	72.2 / 90.8	16.6 / 9.2	78.1 / 94.0	14.7 / 8.4	75.0 / 792.1	22.4 / 11.4
UniAD + EWC $\;{}_{\text{PNAS'17}}$ ?	- / -	- / -	72.3 / 92.3	16.5 / 7.3	80.5 / 95.4	10.0 / 5.3	78.7 / 95.4	14.9 / 4.8
UniAD + SI $\;{}_{\text{PMLR'17}}$ ?	- / -	- / -	69.8 / 88.5	19.8 / 12.0	69.8 / 88.5	9.2 / 8.3	78.1 / 92.0	16.9 / 11.5
UniAD + MAS $\;{}_{\text{ECCV'18}}$ ?	- / -	- / -	72.1 / 90.6	16.7 / 9.4	72.1 / 90.6	14.1 / 8.4	75.4 / 91.8	21.5 / 11.9
UniAD + LVT $\;{}_{\text{CVPR'22}}$ ?	- / -	- / -	70.8 / 94.4	18.3 / 8.4	70.8 / 91.4	13.4 / 8.1	77.5 / 92.3	17.3 / 10.9

Table 2: Task average AUROC (

\uparrow

) and forgetting measurement (FM) (

\downarrow

) on VisA dataset with 4 different task schedules. Each cell shows the image-level/pixel-level AUROC. The best and second best results are marked in bold and underlined , respectively.

Method	MVTec $\rightarrow$ VisA		VisA $\rightarrow$ MVTec
Method	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )
IUF $\;{}_{\text{ECCV'24}}$ ?	82.4 / 92.5	11.1 / 4.9	74.3 / 89.7	16.7 / 5.5
CDAD $\;{}_{\text{CVPR'25}}$ ?	90.0 / 94.9	4.7 / 1.8	84.2 / 94.1	6.9 / 3.5
ContCore (ours)	94.3 / 96.6	0.6 / 0.3	94.0 / 96.7	2.1 / 0.7

Table 3: AUROC and FM results on cross-dataset CAD.

Method	GFLOPS ( $\downarrow$ )	Parameters ( $\downarrow$ )	Test time (ms) ( $\downarrow$ )	Train time (s) ( $\downarrow$ )
DNE $\;{}_{\text{ACMMM'22}}$ ?	70.3	85.8 M	21.3	1233
UCAD $\;{}_{\text{AAAI'24}}$ ?	33.8	68.9 M	15.3	786
IUF $\;{}_{\text{ECCV'24}}$ ?	185.5	28.5 M	80.8	39192
CDAD $\;{}_{\text{CVPR'25}}$ ?	5628.0	11.0 G	2694.0	281434
ContCore (ours)	9.24	45.3M	39.1	754

Table 4: Comparing the efficiency of ContCore and the SOTA baselines in terms of computation, model size, inference speed, and training time.

Task Schedule	Img / Pixel AUROC		Image AP / AUPRO
Task Schedule	Performance ( $\uparrow$ )	FM ( $\downarrow$ )	Performance ( $\uparrow$ )	FM ( $\downarrow$ )
$1\times 30$	90.2 / 97.5	1.7 / 0.6	85.1 / 96.6	1.7 / 0.6
$5\times 6$	90.2 / 97.7	1.4 / 0.3	85 / 96.8	1.6 / 0.3
$10\times 3$	90.3 / 97.6	0.9 / 0.1	85 / 96.7	1.1 / 0.1
10–20	89.9 / 97.5	-0.3 / -0.1	84.6 / 96.6	-0.1 / -0.1
20–10	90.4 / 97.3	-0.2 / -0.3	85 / 96.4	-0.4 / -0.4

Table 5: Performance of ContCore on Real-IAD dataset.

Figure 3: Ablation results of ContCore on MVTecAD by varying coreset size

m

(a), sampling ratio

p

(b), approximation ratio

q

(c), and effect of approximation ratio

q

with large coreset size

m

and sampling ratio

p

(d).

Backbone	MVTecAD		VisA
Backbone	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )
DenseNet201	96.9 / 95.4	0.8 / 0.4	92.7 / 96.4	2.2 / 0.4
DINOv2	97.9 / 93.1	0.0 / 0.0	93.7 / 95.1	0.0 / 0.0
EfficientNet-b1	96.0 / 94.4	0.0 / 0.0	89.6 / 95.8	0.1 / 0.0
WideResNet101	98.5 / 97.2	0.4 / 0.3	94.4 / 97.3	1.2 / 0.5

Table 6: Ablation of ContCore using different backbones.

$T$	$H(\mathcal{O},\;\mathcal{M}_{T})$	$\epsilon_{o}$	$\max_{1\leq k\leq T}\left(\epsilon_{k}+\sum_{j=k}^{T}\widehat{\epsilon}_{j}\right)$	Right hand side of Eq. ( 6 )
2	4.87	2.28	4.37	6.65
3	4.72	2.22	5.99	8.21
4	4.59	2.13	7.54	9.67
5	4.50	2.04	9.02	11.06
6	4.44	2.08	10.66	12.74
7	4.35	2.08	12.27	14.35

Table 7: Theoretical tightness of bound shown for different number of tasks

\{2\leq T\leq 10\}

on MVTecAD classes (tasks) with 12 images from each and the value of each of the terms.

Method	MVTecAD		VisA
Method	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )
DNE $\;{}_{\text{ACMMM'22}}$ ?	69.1 / -	1.3 / -	65.0 / -	3.1 / -
UCAD $\;{}_{\text{AAAI'24}}$ ?	84.2 / 76.2	-1.1 / 0.3	74.6 / 75.8	0.1 / 0.4
IUF $\;{}_{\text{ECCV'24}}$ ?	71.0 / 80.5	0.4 / 0.1	60.3 / 85.0	0.9 / 1.7
CDAD $\;{}_{\text{CVPR'25}}$ ?	66.2 / 80.4	2.0 / 0.1	58.9 / 87.5	0.2 / 0.3
ContCore (ours)	97.2 / 96.7	1.5 / 0.9	94.1 / 97.2	1.3 / 0.8

Table 8: The results on online continual anomaly detection setup.

Method	MVTecAD		VisA
Method	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )
DNE $\;{}_{\text{ACMMM'22}}$ ?	82.6 / -	1.9 / -	61.7 / -	7.4 / -
UCAD $\;{}_{\text{AAAI'24}}$ ?	90.3 / 91.2	4.9 / 3.1	80.7 / 89.8	5.5 / 2.9
IUF $\;{}_{\text{ECCV'24}}$ ?	87.7 / 91.2	5.7 / 4.2	78.0 / 92.9	6.5 / 5.1
CDAD $\;{}_{\text{CVPR'25}}$ ?	90.1 / 93.9	3.3 / 2.7	83.9 / 96.9	3.8 / 1.9
PatchCore Oracle	98.4 / 97.2	0.0 / 0.0	95.1 / 97.6	-0.1 / 0.0

Table 9: Comparison with PatchCore-Oracle.

Sampling Method	Overlapping Samples ( $\uparrow$ )	Average Minimum Distance ( $\downarrow$ )	Sliced Wasserstein Distance ( $\downarrow$ )	Hausdorff Distance ( $\downarrow$ )
Random	356	1.17	0.0260	2.55
MGS	3933	1.04	0.0014	1.93

Table 10: Comparing of coresets of ContCore and random sampling with respect to PatchCore model.

Figure 4: Anomaly segmentation qualitative comparison of ContCore and the SOTA methods by different task schedules on MVTecAD. All samples are from the first task.

Figure 5: Ablation results of ContCore on VisA by varying coreset size

m

(left), sampling ratio

p

(middle), approximation ratio

q

(right).

Method	1 × 15 with 15 Steps		3 × 5 with 5 Steps		10 – 1 × 5 with 6 Steps		10 – 5 with 2 Steps		14 – 1 with 2 Steps
Method	AP ( $\uparrow$ )	FM ( $\downarrow$ )	AP ( $\uparrow$ )	FM ( $\downarrow$ )	AP ( $\uparrow$ )	FM ( $\downarrow$ )	AP ( $\uparrow$ )	FM ( $\downarrow$ )	AP ( $\uparrow$ )	FM ( $\downarrow$ )
DNE $\;{}_{\text{ACMMM'22}}$ ?	75.9	10.2	76.4	4.4	71.4	9.8	77.0	2.6	76.0	1.9
UCAD $\;{}_{\text{AAAI'24}}$ ?	95.3	0.0	93.1	2.0	95.5	0.1	95.0	1.0	95.8	0.3
IUF $\;{}_{\text{ECCV'24}}$ ?	84.1	2.0	91.1	2.9	95.3	0.2	95.4	1.9	97.8	0.3
CDAD $\;{}_{\text{CVPR'25}}$ ?	86.3	8.2	95.8	1.9	98.4	0.0	98.3	0.6	98.4	0.1
ContCore ( ours )	99.3	0.3	99.3	0.2	99.7	0.0	99.5	0.1	99.4	0.0

Table 11: The results of MVTecAD dataset on few step incremental schedules where we report task mean average precision (AP) (

\uparrow

) and forgetting measurement (FM) (

\downarrow

). The best and second best results are marked in bold and underlined , respectively.

Method	1 × 15 with 15 Steps		3 × 5 with 5 Steps		10 – 1 × 5 with 6 Steps		10 – 5 with 2 Steps		14 – 1 with 2 Steps
Method	AUPRO ( $\uparrow$ )	FM ( $\downarrow$ )	AUPRO ( $\uparrow$ )	FM ( $\downarrow$ )	AUPRO ( $\uparrow$ )	FM ( $\downarrow$ )	AUPRO ( $\uparrow$ )	FM ( $\downarrow$ )	AUPRO ( $\uparrow$ )	FM ( $\downarrow$ )
UCAD $\;{}_{\text{AAAI'24}}$ ?	87.6	-0.8	71.1	7.5	80.8	1.2	80.7	2.9	86.3	1.2
IUF $\;{}_{\text{ECCV'24}}$ ?	78.7	6.8	72.9	5.8	84.3	2.4	85.0	3.2	88.6	0.6
CDAD $\;{}_{\text{CVPR'25}}$ ?	68.5	22.1	83.8	4.1	89.2	1.2	88.9	2.6	89.8	0.5
ContCore ( ours )	99.3	0.3	96.3	0.2	99.7	0.0	96.0	0.2	96.5	0.0

Table 12: The results of MVTecAD dataset on few step incremental schedules where we report task average PRO (

\uparrow

) and forgetting measurement (FM) (

\downarrow

). The best and second best results are marked in bold and underlined , respectively.

Method	1 × 12 with 12 Steps		8 – 1 × 4 with 5 Stepss		8 – 4 with 2 Steps		11 – 1 with 2 Steps
Method	AP ( $\uparrow$ )	FM ( $\downarrow$ )	AP ( $\uparrow$ )	FM ( $\downarrow$ )	AP ( $\uparrow$ )	FM ( $\downarrow$ )	AP ( $\uparrow$ )	FM ( $\downarrow$ )
DNE $\;{}_{\text{ACMMM'22}}$ ?	65.1	11.6	66.1	1.3	67.3	9.8	70.8	1.6
UCAD $\;{}_{\text{AAAI'24}}$ ?	81.7	0.0	82.9	2.2	83.2	5.2	88.1	0.3
IUF $\;{}_{\text{ECCV'24}}$ ?	88.8	1.2	83.0	6.9	83.4	7.5	91.6	-0.0
CDAD $\;{}_{\text{CVPR'25}}$ ?	67.6	14.0	84.7	1.8	85.3	3.1	89.4	-0.8
ContCore ( ours )	96.5	0.1	94.1	1.8	96.7	0.4	94.8	0.6

Table 13: The results of VisA dataset on few step incremental schedules where we report task mean average precision (AP) (

\uparrow

) and forgetting measurement (FM) (

\downarrow

). The best and second best results are marked in bold and underlined , respectively.

Method	1 × 12 with 12 Steps		8 – 1 × 4 with 5 Stepss		8 – 4 with 2 Steps		11 – 1 with 2 Steps
Method	AUPRO ( $\uparrow$ )	FM ( $\downarrow$ )	AUPRO ( $\uparrow$ )	FM ( $\downarrow$ )	AUPRO ( $\uparrow$ )	FM ( $\downarrow$ )	AUPRO ( $\uparrow$ )	FM ( $\downarrow$ )
UCAD $\;{}_{\text{AAAI'24}}$ ?	80.0	0.5	70.5	9.8	72.4	7.5	80.4	2.0
IUF $\;{}_{\text{ECCV'24}}$ ?	83.3	4.7	57.0	24.0	63.9	20.8	82.0	1.0
CDAD $\;{}_{\text{CVPR'25}}$ ?	59.0	17.0	77.7	1.7	78.9	1.6	81.6	-0.2
ContCore ( ours )	96.5	0.1	94.1	1.8	96.7	0.4	94.8	0.6

Table 14: The results of VisA dataset on few step incremental schedules where we report task average PRO (

\uparrow

) and forgetting measurement (FM) (

\downarrow

). The best and second best results are marked in bold and underlined , respectively.

Backbone	1 × 15 with 15 Steps		3 × 5 with 5 Steps		10 – 1 × 5 with 6 Steps		10 – 5 with 2 Steps		14 – 1 with 2 Steps
Backbone	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )
DenseNet201	98.1 / 96.8	0.8 / 0.3	95.7 / 95.1	1.7 / 1.0	98.4 / 95.7	0.5 / 0.2	95.5 / 93.3	1.2 / 0.6	97.0 / 96.1	0.0 / 0.0
DINOv2	97.6 / 94.3	0.0 / 0.0	97.4 / 93.9	-0.0 / 0.0	98.5 / 92.3	0.0 / 0.0	97.6 / 92.8	0.0 / 0.0	98.2 / 92.4	0.0 / 0.0
EfficientNet b1	95.2 / 95.0	0.0 / 0.0	95.5 / 94.2	0.0 / -0.0	96.7 / 94.2	0.0 / 0.0	96.1 / 94.0	0.0 / -0.0	96.6 / 94.8	0.0 / 0.0
WideResNet101	99.0 / 97.8	0.1 / 0.1	97.2 / 96.7	0.9 / 0.6	99.0 / 96.9	0.3 / 0.2	98.4 / 96.8	0.7 / 0.6	98.8 / 97.6	0.1 / 0.0

Table 15: Full restuls of ablation on different backbones on MVTecAD dataset.

Backbone	1 × 12 with 12 Steps		8 – 1 × 4 with 5 Stepss		8 – 4 with 2 Steps		11 – 1 with 2 Steps
Backbone	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )
DenseNet201	91.8 / 96.9	2.4 / 0.5	94.4 / 96.6	2.2 / 0.4	91.0 / 95.2	3.0 / 0.6	93.6 / 96.9	1.1 / 0.2
DINOv2	92.8 / 95.0	0.0 / 0.0	94.2 / 94.3	0.1 / 0.0	93.3 / 94.5	-0.0 / -0.0	94.3 / 96.5	0.0 / -0.0
EfficientNet b1	88.3 / 95.3	0.0 / 0.0	91.0 / 96.1	0.0 / 0.0	88.2 / 95.3	0.2 / -0.0	90.8 / 96.3	0.0 / -0.0
WideResNet101	93.8 / 97.4	1.5 / 0.5	96.5 / 97.4	0.8 / 0.4	92.7 / 96.8	2.4 / 1.1	94.6 / 97.4	-0.0 / 0.0

Table 16: Full restuls of ablation on different backbones on VisA dataset.

Coreset Size $m$	1 × 15 with 15 Steps		3 × 5 with 5 Steps		10 – 1 × 5 with 6 Steps		10 – 5 with 2 Steps		14 – 1 with 2 Steps
Coreset Size $m$	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )
5,000	94.2 / 93.8	4.1 / 2.9	90.9 / 94.1	3.5 / 1.2	96.0 / 94.8	1.7 / 0.6	93.0 / 94.1	2.1 / 0.7	96.5 / 95.9	0.2 / 0.0
10,000	94.7 / 96.2	4.3 / 1.5	95.1 / 95.6	1.8 / 0.7	98.5 / 95.8	0.5 / 0.2	97.1 / 95.7	0.8 / 1.0	97.7 / 96.4	-0.1 / 0.0
15,000	98.3 / 97.0	0.9 / 0.6	95.4 / 96.2	1.5 / 0.3	99.0 / 96.1	0.1 / 0.4	97.2 / 96.1	1.6 / 0.9	98.0 / 96.8	0.0 / 0.0
20,000	98.8 / 97.4	0.2 / 0.2	98.2 / 96.9	0.6 / 0.5	98.9 / 96.4	0.3 / 0.2	97.7 / 96.6	0.5 / 0.5	98.3 / 97.3	0.0 / 0.1
25,000	99.0 / 97.3	0.1 / 0.2	97.7 / 96.9	0.3 / 0.3	98.9 / 96.9	0.1 / 0.1	98.3 / 97.2	0.5 / 0.2	98.7 / 97.7	-0.0 / 0.0

Table 17: Full results of ablation on corset size

m

on MVTecAD dataset.

Coreset Size $m$	1 × 12 with 12 Steps		8 – 1 × 4 with 5 Stepss		8 – 4 with 2 Steps		11 – 1 with 2 Steps
Coreset Size $m$	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )
10,000	86.5 / 94.5	6.9 / 2.1	93.2 / 95.3	3.2 / 1.3	89.0 / 94.5	3.4 / 1.0	92.3 / 96.2	0.9 / -0.2
20,000	90.6 / 96.1	4.3 / 1.6	95.0 / 96.2	1.3 / 1.0	92.3 / 95.9	1.8 / 1.0	94.8 / 96.8	0.2 / -0.2
30,000	92.6 / 96.8	2.1 / 0.9	95.2 / 96.6	2.1 / 0.7	93.3 / 96.4	1.4 / 0.6	94.8 / 97.1	-0.2 / 0.1
40,000	93.7 / 97.2	0.7 / 0.6	96.3 / 97.2	0.7 / 0.5	93.8 / 96.5	1.2 / 0.8	96.1 / 97.7	0.4 / -0.0
50,000	94.0 / 97.5	0.4 / 0.2	97.0 / 97.4	0.5 / 0.4	94.6 / 97.2	0.5 / 0.2	96.2 / 97.8	0.4 / -0.0

Table 18: Full results of ablation on corset size

m

on VisA dataset.

Sampling Ratio $p$	1 × 15 with 15 Steps		3 × 5 with 5 Steps		10 – 1 × 5 with 6 Steps		10 – 5 with 2 Steps		14 – 1 with 2 Steps
Sampling Ratio $p$	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )
0.01	98.8 / 97.4	0.2 / 0.2	98.2 / 96.9	0.6 / 0.5	98.9 / 96.4	0.3 / 0.2	97.7 / 96.6	0.5 / 0.5	98.3 / 97.3	0.0 / 0.1
0.05	97.5 / 96.6	1.4 / 1.3	96.6 / 96.7	1.6 / 0.7	98.4 / 96.7	0.9 / 0.3	97.8 / 96.7	0.4 / 0.8	97.5 / 97.1	0.1 / 0.0
0.1	97.1 / 96.8	1.7 / 1.1	96.6 / 96.7	1.6 / 0.7	98.8 / 96.5	0.5 / 0.4	97.8 / 96.7	0.4 / 0.8	97.5 / 97.1	0.1 / 0.0
0.25	97.4 / 96.6	1.5 / 1.4	96.6 / 96.7	1.6 / 0.7	98.9 / 96.6	0.5 / 0.3	97.8 / 96.7	0.4 / 0.8	97.6 / 97.1	0.0 / -0.0
0.5	97.5 / 96.6	1.4 / 1.3	96.6 / 96.7	1.6 / 0.7	98.9 / 96.7	0.5 / 0.3	97.8 / 96.7	0.4 / 0.8	97.6 / 97.1	0.0 / -0.0

Table 19: Full results of ablation on sampling ratio

p

on MVTecAD dataset.

Sampling Ratio $p$	1 × 12 with 12 Steps		8 – 1 × 4 with 5 Stepss		8 – 4 with 2 Steps		11 – 1 with 2 Steps
Sampling Ratio $p$	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )
0.01	93.7 / 97.2	0.7 / 0.6	96.3 / 97.2	0.7 / 0.5	93.8 / 96.5	1.2 / 0.8	96.1 / 97.7	0.4 / -0.0
0.05	92.4 / 96.6	2.7 / 1.1	96.1 / 97.3	1.1 / 0.6	93.9 / 97.0	1.1 / 0.6	95.5 / 97.4	0.1 / -0.1
0.1	91.8 / 96.1	3.2 / 1.5	95.9 / 97.4	1.7 / 0.5	93.9 / 97.0	1.1 / 0.6	95.3 / 97.3	-0.2 / -0.0
0.25	91.8 / 95.7	3.2 / 2.0	95.9 / 97.3	1.7 / 0.5	93.9 / 97.0	1.1 / 0.6	95.3 / 97.4	-0.2 / -0.0
0.5	91.8 / 95.7	3.2 / 2.0	95.9 / 97.3	1.7 / 0.5	93.9 / 97.0	1.1 / 0.6	95.3 / 97.4	-0.2 / -0.0

Table 20: Full results of ablation on sampling ratio on VisA dataset.

Method	1 × 15 with 15 Steps		3 × 5 with 5 Steps		10 – 1 × 5 with 6 Steps		10 – 5 with 2 Steps		14 – 1 with 2 Steps
Method	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )
DNE $\;{}_{\text{ACMMM'22}}$ ?	76.3 / -	2.4 / -	70.9 / -	2.6 / -	66.2 / -	1.0 / -	67.2 / -	0.3 / -	64.7 / -	0.2 / -
UCAD $\;{}_{\text{AAAI'24}}$ ?	91.0 / 83.3	0.0 / 0.8	81.2 / 75.3	0.1 / 1.3	89.9 / 74.7	0.7 / 1.5	78.6 / 77.3	-1.0 / 3.5	80.3 / 70.6	-5.3 / -5.4
IUF $\;{}_{\text{ECCV'24}}$ ?	69.4 / 78.2	0.1 / 0.2	71.0 / 80.3	0.4 / 0.1	71.9 / 78.9	0.8 / 0.1	72.1 / 81.7	0.0 / 0.1	70.8 / 83.4	0.5 / -0.2
CDAD $\;{}_{\text{CVPR'25}}$ ?	67.8 / 76.0	2.7 / 0.0	60.4 / 77.0	3.7 / 0.2	62.4 / 77.5	4.7 / 0.0	70.5 / 83.1	-0.9 / 0.1	69.9 / 88.2	0.0 / 0.0
ContCore	96.7 / 96.7	2.1 / 1.2	96.3 / 96.4	2.5 / 1.3	98.4 / 96.4	0.6 / 0.4	96.8 / 96.5	2.3 / 1.2	98.0 / 97.5	-0.1 / 0.2

Table 21: Full results of ablation on online continual on MVTecAD dataset.

Method	1 × 12 with 12 Steps		8 – 1 × 4 with 5 Stepss		8 – 4 with 2 Steps		11 – 1 with 2 Steps
Method	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )
DNE $\;{}_{\text{ACMMM'22}}$ ?	61.7 / 0.0	3.7 / 0.0	62.8 / 0.0	3.2 / 0.0	65.6 / 0.0	5.7 / 0.0	69.7 / 0.0	-0.3 / 0.0
UCAD $\;{}_{\text{AAAI'24}}$ ?	78.3 / 76.9	0.0 / 0.0	75.4 / 79.8	-0.2 / 0.5	66.7 / 73.1	-0.2 / 0.2	78.0 / 73.3	0.7 / 1.0
IUF $\;{}_{\text{ECCV'24}}$ ?	58.0 / 80.4	0.2 / -0.0	57.0 / 84.8	6.0 / 2.7	64.9 / 87.1	-1.4 / 0.9	61.4 / 87.7	-1.2 / 3.2
CDAD $\;{}_{\text{CVPR'25}}$ ?	57.3 / 83.5	0.1 / 0.0	56.3 / 86.2	1.3 / 0.6	60.0 / 86.9	-1.6 / -0.5	61.9 / 93.3	0.9 / 0.9
ContCore	91.8 / 96.6	2.3 / 1.1	96.2 / 97.4	0.3 / 0.6	93.1 / 97.0	1.8 / 1.0	95.1 / 97.8	0.8 / 0.3

Table 22: Full results of ablation on online continual on VisA dataset.

Approximate Ratio $q$	1 × 15 with 15 Steps			3 × 5 with 5 Steps			10 – 1 × 5 with 6 Steps			10 – 5 with 2 Steps			14 – 1 with 2 Steps
Approximate Ratio $q$	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	Time ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	Time ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	Time ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	Time ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	Time ( $\downarrow$ )
0	98.8 / 97.2	0.3 / 0.2	95	97.2 / 96.7	0.5 / 0.3	131	98.7 / 96.6	0.3 / 0.2	163	97.9 / 96.6	1.3 / 0.6	172	98.4 / 97.5	-0.0 / 0.0	173
0.25	98.8 / 97.3	0.2 / 0.2	90	97.3 / 96.7	0.5 / 0.3	129	98.8 / 96.5	0.4 / 0.2	158	97.2 / 96.7	1.9 / 0.7	169	98.0 / 97.4	0.2 / -0.0	171
0.5	98.3 / 97.2	0.8 / 0.2	83	97.0 / 96.7	0.6 / 0.4	127	98.6 / 96.3	0.5 / 0.3	151	97.5 / 96.7	1.5 / 0.6	167	97.8 / 97.5	0.1 / 0.0	168
0.75	98.8 / 97.4	0.2 / 0.2	79	98.2 / 96.9	0.6 / 0.5	126	98.9 / 96.4	0.3 / 0.2	147	97.7 / 96.6	0.5 / 0.5	165	98.3 / 97.3	0.0 / 0.1	167
1	96.4 / 96.6	0.8 / 0.3	75	95.1 / 95.2	0.7 / 0.4	124	86.9 / 91.0	1.2 / 0.3	142	94.5 / 94.1	1.8 / 1.1	164	72.6 / 91.7	0.7 / 0.0	165

Table 23: Full results of ablation on approximate ratio on MVTecAD dataset.

Approximate Ratio $q$	1 × 12 with 12 Steps			8 – 1 × 4 with 5 Stepss			8 – 4 with 2 Steps			11 – 1 with 2 Steps
Approximate Ratio $q$	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	Time ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	Time ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	Time ( $\downarrow$ )	AUROC ( $\uparrow$ )	FM ( $\downarrow$ )	Time ( $\downarrow$ )
0	93.3 / 97.2	1.2 / 0.5	372	96.2 / 97.2	1.2 / 0.5	452	94.0 / 97.1	2.1 / 0.5	458	95.7 / 97.5	-0.1 / -0.1	458
0.25	93.2 / 97.3	1.2 / 0.4	352	96.6 / 97.5	1.1 / 0.4	432	94.3 / 97.2	1.5 / 0.2	442	96.0 / 97.5	-0.2 / -0.0	442
0.5	93.4 / 97.2	1.0 / 0.5	333	96.7 / 97.3	0.8 / 0.5	412	94.0 / 97.2	1.9 / 0.2	426	96.0 / 97.6	0.4 / -0.1	427
0.75	93.7 / 97.2	0.7 / 0.6	319	96.3 / 97.2	0.7 / 0.5	399	93.8 / 96.5	1.2 / 0.8	414	96.1 / 97.7	0.4 / -0.0	416
1	90.7 / 96.7	3.8 / 1.0	311	87.1 / 95.8	0.9 / 0.5	391	84.9 / 94.3	2.1 / 0.6	408	91.8 / 96.1	2.8 / 0.0	408

Table 24: Full results of ablation on approximate ratio on VisA dataset.

Method	Task AVG	FM
Reservoir	92.2 / 96.5	1.4 / 0.3
Herding	90.1 / 96.3	2.0 / 0.6
Ours	98.4 / 96.9	0.3 / 0.3

Table 25: Averaged results of MVTecAD dataset with 5 schedules with different sampling methods with (image/pixel) format.

Figure 6: The qualitative results with $1\times 15$ task schedule on the MVTecAD dataset.

Figure 7: The qualitative results with 10 – 5 task schedule on the MVTecAD dataset.

Figure 8: The qualitative results with 10 – 1 $\times$ 5 task schedule on the MVTecAD dataset.

Figure 9: The qualitative results with 14 – 1 task schedule on the MVTecAD dataset.

Figure 10: The qualitative results with 3 $\times$ 5 task schedule on the MVTecAD dataset.

Continual Anomaly Detection

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TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data
Jun 10, 2026Dayananda Herurkar, Federico Raue, Joachim Folz +2Continual Anomaly Detection Tabular Anomaly Detection
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany · RPTU Kaiserslautern-Landau, Germany · Hochschule Bonn-Rhein-Sieg (H-BRS), Germany
Neural-Collapse-guided Task-Free Continual Anomaly Detection
Sep 3, 2026Xiaotong Kong, Chaoyang Song, Ziai Zhou +3Continual Anomaly Detection Continual Learning
Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, China
ProCon: Projection-Consistency Memory for Training-Free Anomaly Detection
Jul 6, 2026Joongwon Chae, Lihui Luo, Yang Liu +4Industrial Anomaly Detection Anomaly Localization
Tsinghua University Shenzhen International Graduate School, Shenzhen, China · Affiliated Fifth Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China · Chinese Medicine Guangdong Laboratory +1

Jun 10, 2026J/K move · Enter open · S save

TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data

Dayananda Herurkar, Federico Raue, Joachim Folz, Jörn Hees+1

German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany · RPTU Kaiserslautern-Landau, Germany · Hochschule Bonn-Rhein-Sieg (H-BRS), Germany

Continual Anomaly Detection Tabular Anomaly Detection Continual Learning OOD Detection Anomaly Detection

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Continual anomaly detection in tabular data is challenging and remains largely underexplored, particularly in settings with heterogeneous feature schemas, distribution shifts, and severe class imbalance. In many real-world applications, data arrive sequentially from diverse domains, rendering conventional continual learning methods ineffective due to their reliance on a fixed input space. We propose a continual learning (CL) method, which can overcome these challenges and continually learn from different tasks. Our method consists of three main parts: our AGF model, Taskfusion augmentation, and outlier exposure. The AGF-model maps task-specific features into a shared space, then aligns distributions to reduce representation drift, and learns anomaly decision boundaries in the aligned space. To improve stability, we introduce Taskfusion augmentation, combining boundary-aware interpolation within tasks to refine the model anomaly boundaries and cross-task mixing to transfer anomaly structure across datasets. To handle class imbalance and memory constraints, we employ tabular dataset distillation to store compact synthetic replay samples, which are jointly used with augmented data in an outlier exposure objective for robust anomaly detection. We evaluate the approach on 21 heterogeneous datasets across multiple domains. Results show that our approach substantially improves continual anomaly detection performance over sequential fine-tuning and other CL baselines while reducing catastrophic forgetting and maintaining stable detection across heterogeneous datasets.

Abstract

Continual Anomaly Detection

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