Weakly Supervised Anomaly Detection via Dual-Tailed Kernel
Authors: Walid Durani, Tobias Nitzl, Claudia Plant, Christian Böhm
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, WSAD-DT achieves state-of-the-art performance on several challenging anomaly detection benchmarks, outperforming leading ensemble-based methods such as XGBOD. ... 8. Experiments 8.1. Experimental Setup We compare WSAD-DT with state-of-the-art deep anomaly detection methods on over 20 real-world datasets from the Ad Benchmark repository (Han et al., 2022). Each dataset is split into 70% training and 30% testing, preserving the anomaly ratio via stratified sampling. |
| Researcher Affiliation | Academia | 1LMU Munich, Munich Center for Machine Learning (MCML), Munich, Germany 2LMU Munich, Munich, Germany 3Faculty of Computer Science, ds:Uni Vie, University of Vienna, Vienna, Austria 4Faculty of Computer Science, University of Vienna, Vienna, Austria. |
| Pseudocode | Yes | G. Algorithm details In Algo. 1 we describe WSAD-DT in detail. |
| Open Source Code | No | Our code is implemented in Py Torch and builds on top of the Deep OD and Py OD libraries (Zhao et al., 2019; Xu, 2023). Our anonymous code repository: Link (Anonymous). |
| Open Datasets | Yes | We compare WSAD-DT with state-of-the-art deep anomaly detection methods on over 20 real-world datasets from the Ad Benchmark repository (Han et al., 2022). |
| Dataset Splits | Yes | Each dataset is split into 70% training and 30% testing, preserving the anomaly ratio via stratified sampling. |
| Hardware Specification | Yes | All experiments were conducted on a workstation equipped with an Intel Core i7-10700K CPU (3.8 GHz) and 32 GB of RAM. |
| Software Dependencies | No | Our code is implemented in Py Torch and builds on top of the Deep OD and Py OD libraries (Zhao et al., 2019; Xu, 2023). All models are trained for 100 epochs using the Adam optimizer with a learning rate of 1e-3 and a weight decay of 1e-5. We use the standard Adam hyperparameters (β1 = 0.9, β2 = 0.999). |
| Experiment Setup | Yes | All models are trained for 100 epochs using the Adam optimizer with a learning rate of 1e-3 and a weight decay of 1e-5. We use the standard Adam hyperparameters (β1 = 0.9, β2 = 0.999). Batches of size 64 are used for each training step (Table 7). ... Table 7. Neuralnetwork and training setting of WSAD-DT: GENERAL TRAINING Batch size 64 Learning rate 1e-3 Epochs 100 OPTIMIZER Optimizer Adam Momentum β1 0.9 Momentum β2 0.999 Weight decay 1e-5 |