Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection
Authors: Vercruyssen Vincent, Meert Wannes, Davis Jesse6054-6061
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, LOCIT outperforms a multitude of existing transfer learning and anomaly detection methods on a new transfer learning benchmark for anomaly detection. Moreover, it outperforms its competitors on a real-world anomaly detection problem of identifying anomalous water usage in multiple retail stores. |
| Researcher Affiliation | Academia | Vincent Vercruyssen, Wannes Meert, Jesse Davis DTAI group, KU Leuven, Belgium firstname.lastname@kuleuven.be |
| Pseudocode | Yes | Algorithm 1: LOCIT(DS, DT , ψ, k) |
| Open Source Code | Yes | Finally, we provide an implementation of LOCIT.1 https://github.com/Vincent-Vercruyssen/LocIT |
| Open Datasets | Yes | The code, benchmark data, elaborated explanations, full parameter settings, and further experiments are available in an online appendix.2 https://github.com/Vincent-Vercruyssen/LocIT. We start from one of 12 publicly available, multi-class master datasets. To generate a target domain, we sample normal (anomalous) target instances from the largest (second largest) class in the master dataset. |
| Dataset Splits | No | The paper mentions "threefold cross-validation" for tuning the SVM's hyperparameters within the LOCIT algorithm, but it does not describe a separate validation set split (e.g., 80/10/10) for the overall model evaluation or hyperparameter tuning of the main anomaly detection task itself. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using an SVM and KNN, which are algorithms, but does not specify any software dependencies with version numbers (e.g., specific Python libraries or frameworks like scikit-learn or PyTorch versions). |
| Experiment Setup | Yes | LOCIT has two hyperparameters. We set the neighborhood size ψ = 20 and k = 10 in SSKNNO. LOCIT tunes the SVMs hyperparameters using threefold cross-validation on the generated training data. It selects either a linear or Gaussian kernel and sets C [0.01, 0.1, 0.5, 1, 10, 100] for both kernels and σ [0.01, 0.1, 0.5, 1, 10, 100] for the Gaussian kernel. We simply use the baselines with the hyperparameters recommended in the original papers or in comparative studies. |