Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Latent Outlier Exposure for Anomaly Detection with Contaminated Data
Authors: Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt
ICML 2022 | Venue PDF | 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. |