Latent Outlier Exposure for Anomaly Detection with Contaminated Data
Authors: Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments with several backbone models on three image datasets, 30 tabular data sets, and a video anomaly detection benchmark showed consistent and significant improvements over the baselines. |
| Researcher Affiliation | Collaboration | 1Bosch Center for Artificial Intelligence 2TU Kaiserslautern, Germany 3UC Irvine, USA. |
| Pseudocode | Yes | Algorithm 1 Training process of LOE |
| Open Source Code | Yes | 1Code is available at https://github.com/ boschresearch/Latent OE-AD.git |
| Open Datasets | Yes | We experiment with three image datasets: CIFAR-10, Fashion-MNIST, and MVTEC (Bergmann et al., 2019)... We study all 30 tabular datasets used in the empirical analysis of a recent state-of-theart paper (Shenkar & Wolf, 2022)... We study UCSD Peds17, a popular benchmark for video anomaly detection. |
| Dataset Splits | Yes | On CIFAR-10 and F-MNIST, we follow the standard one-vs.-rest protocol... We follow the pre-processing and train-test split of the datasets in Shenkar & Wolf (2022). |
| Hardware Specification | No | The paper mentions that experiments were performed on "GPU clusters" in the acknowledgements, but does not provide specific details such as GPU models, CPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions using "Adam (Kingma & Ba, 2014) stochastic optimizer" and various backbone models like "Res Net" but does not specify version numbers for these or other software libraries/frameworks. |
| Experiment Setup | Yes | During training, we used Adam (Kingma & Ba, 2014) stochastic optimizer and set the mini-batch size to be 25. The learning rate is 0.01, and we trained the model for 200 epochs... On CIFAR-10, we set minibatch size to be 500, learning rate to be 4e-4, 30 training epochs with Adam optimizer... On MVTEC, we set minibatch size to be 40, learning rate to be 2e-4, 30 training epochs with Adam optimizer. |