Organizations: Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong · College of Computer Science, Sichuan University, China
Weakly supervised anomaly detection on tabular data is an essential task in many label-scarce applications, since tabular data is widely encountered and it is commonly observed in practice that only a limited number of labeled anomalies are available alongside abundant unlabeled samples. Most existing methods try to learn features that capture the inherent distribution patterns in the target domain. However, such features often struggle to provide a complete description of possible abnormal characteristics and are highly susceptible to overfitting the extremely scarce labels. To address this challenge, we propose a weakly supervised anomaly detection method based on universal semantic enhancement for tabular data, which fuses the universal knowledge from a pre-trained tabular foundation model with the domain-specific knowledge learned on the target dataset. The two types of knowledge are naturally complementary: universal semantics describe the common patterns of tabular data across domains, while the domain-specific features capture the inherent distributions of normal and anomalous behaviors in the target dataset. A prototype-conditioned modulation mechanism is then designed to effectively integrate the two types of features, allowing them to reinforce each other for improved anomaly detection. Extensive experiments on public datasets show that the proposed method outperforms competitive methods when only eight labeled anomalies are available in the training procedure.
Figures & tables
Figure 1: Overview of the proposed framework. Universal semantic features ti are extracted by the frozen pre-trained model, and domain-specific features (hi,ri,ei) by the AutoEncoder. Our ProtoFiLM-Dynamic fuses hi and ti via per-sample prototype attention to produce the fused representation fi . The scoring network takes (ri,fi,ei) as input and outputs the anomaly score si . During training, label interpolation between a labeled anomaly and an unlabeled sample with coefficient α generates a pseudo-sample xmix whose score is supervised toward the interpolated soft target αa ( a is the score margin); at inference, a zero-parameter score ensemble combines the anomaly score with the pre-trained model’s class probability pt into the final score sfinal .
Dataset
RF
CatBoost
DevNet
FEAWAD
PReNet
DeepSAD
TabPFN
Ours
ALOI
0.5130
0.5270
0.5106
0.5187
0.4953
0.5388
0.6098
0.6466
Campaign
0.5323
0.8741
0.5868
0.5893
0.6809
0.6329
0.8570
0.8668
Cardio
0.9256
0.9722
0.8578
0.9469
0.9333
0.9545
0.9870
0.9964
Cardiotocography
0.8212
0.9239
0.8060
0.8171
0.8659
0.8709
0.9340
0.9424
Fault
0.6519
0.7366
0.5863
0.6286
0.6086
0.7574
0.7624
0.7826
Hepatitis
0.8385
0.8154
0.7718
0.7000
0.7846
0.6820
0.8564
0.8827
Table 1: Per-dataset AUROC on 20 representative ADBench datasets ( ∣M∣=8 , 10 runs). Bold = best, underline = second best. OOM = GPU out of memory. Avg. Rank (20) is computed over the 20 datasets shown, and Avg. Rank (42) over all 42 evaluated datasets.
Figure 2: AUROC (top) and AUPRC (bottom) vs. number of labeled anomalies ∣M∣ on five representative datasets. Ours and TabPFN are shown with solid thick lines; all other baselines are shown with semi-transparent dashed lines.
Configuration
AUROC
AUPRC
Δ
Baseline (concat + dev loss)
0.9174
0.7750
—
+ FiLM (static prototype)
0.9178
0.7703
+0.0004
+ ProtoFiLM-Dynamic
0.9238
0.7775
+0.0064
+ RoSAS (loss only)
0.9183
0.7745
+0.0009
+ PDF (score fusion only)
0.9242
0.7782
+0.0068
+ ProtoFiLM-Dynamic + PDF
0.9247
0.7776
+0.0073
Table 3: Component ablation. Each row adds one design choice to the previous configuration. Δ is relative to the ablation baseline AUROC (0.9174).
Figure 3: t-SNE [ 23 ] of the 32-dim penultimate scoring layer on the Waveform dataset ( ∣M∣=8 , one run). Blue: normal samples ( n=669 ); orange: anomalies ( n=20 ). Anomaly points in the raw feature space (left) are indistinguishable from normals. The Concat baseline (center) produces partial clustering but with significant normal–anomaly mixing. ProtoFiLM-Dynamic (right) yields a tight, spatially isolated anomaly cluster with a clean decision boundary.
Figure 4: Sensitivity of AUROC (left axis, blue) and AUPR (right axis, orange) to the RoSAS loss weight λ on 42 datasets ( ∣M∣=8 , 10 runs). Dashed lines mark the ablation baseline (concat + dev loss) without RoSAS.
Anomaly detection on tabular data is commonly studied under three supervision regimes, including one-class settings that assume access to anomaly-free training samples, fully unsupervised settings with unlabeled and potentially contaminated training data, and semi-supervised settings with limited anomaly labels. Existing deep learning approaches typically train dataset-specific models under the assumption of a single supervision regime, which limits their ability to leverage shared structures across anomaly detection tasks and to adapt to different supervision levels. We propose ICLAD, an in-context learning foundation model for tabular anomaly detection that generalizes across both datasets and supervision regimes. ICLAD is trained via meta-learning on synthetic tabular anomaly detection tasks, and at inference time, the model assigns anomaly scores by conditioning on the training set without updating model weights. Comprehensive experiments on 57 tabular datasets from ADBench show that our method achieves state-of-the-art performance across three supervision regimes, establishing a unified framework for tabular anomaly detection.
Jack Yi Wei, Narges Armanfard
Department of Electrical and Computer Engineering, McGill University, Canada · Mila - Quebec Artificial Intelligence Institute, Montreal, Canada
Identifying anomalous instances in tabular data is essential for improving data reliability and maintaining system stability. Due to the scarcity of ground-truth anomaly labels, existing methods mainly rely on unsupervised anomaly detection models, or exploit a small number of labeled anomalies to facilitate detection via sample generation or contrastive learning. However, unsupervised methods lack sufficient anomaly awareness, while current generation and contrastive approaches tend to compute anomalies globally, overlooking the localized anomaly patterns of tabular features, resulting in suboptimal detection performance. To address these limitations, we propose PLAG, a pseudo-label-guided anomaly generation method designed to enhance tabular anomaly detection. Specifically, by utilizing pseudo-anomalies as guidance signals and decoupling the overall anomaly quantification of a sample into an accumulation of feature-level abnormalities, PLAG not only effectively obviates the need for scarce ground-truth labels but also provides a novel perspective for the model to comprehend localized anomalous signals at a fine-grained level. Furthermore, a two-stage data selection strategy is proposed, integrating format verification and uncertainty estimation to rigorously filter candidate samples, thereby ensuring the fidelity and diversity of the synthetic anomalies. Ultimately, these filtered synthetic anomalies serve as robust discriminative guidance, empowering the model to better separate normal and anomalous instances. Extensive experiments demonstrate that PLAG achieves state-of-the-art performance against eight representative baselines. Moreover, as a flexible framework, it integrates seamlessly with existing unsupervised detectors, consistently boosting F1-scores by 0.08 to 0.21.
Weakly supervised anomaly detection (WSAD) has developed in three primary directions: incomplete, inexact, and inaccurate supervision. However, these directions remain isolated, lacking a unified framework to assess whether they address unique challenges or share fundamental mechanisms. This paper introduces WSADBench, the first benchmark that unifies evaluation across distinct weakly supervised scenarios, benchmarking diverse approaches from specialized WSAD methods to advanced tabular foundation models. WSADBench establishes standardized protocols to evaluate 36 algorithms across 4 modalities by systematically varying label quantity, granularity, and quality, revealing the performance boundaries of various methods. Based on over 700K experiments, WSADBench reveals four critical insights: (i) Strong intrinsic correlations exist between these weak supervision scenarios, challenging the isolation of current research directions. (ii) Specialized WSAD algorithms excel only in extreme label-scarcity regimes but are quickly dominated by tabular foundation models and general classification methods as supervision increases or in OOD scenarios. (iii) Unlabeled data shows inconsistent utility across settings, with marginal gains compared to label refinement. (iv) Models exhibit asymmetric sensitivity to different types of label noise. We release WSADBench as an open-source benchmark with code and datasets to facilitate future WSAD research: https://github.com/SUFE-AILAB/WSADBench.
Xu Yao, Siyuan Zhou, Zhenbo Wu +6
Shanghai University of Finance and Economics · Ant Group · Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics