Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection
Authors: Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, Haifeng Chen
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on several public benchmark datasets show that, DAGMM significantly outperforms state-of-the-art anomaly detection techniques, and achieves up to 14% improvement based on the standard F1 score. |
| Researcher Affiliation | Collaboration | NEC Laboratories America Washington State University, Pullman {bzong, renqiang, weicheng, lume, dkcho, haifeng}@nec-labs.com qsong@eecs.wsu.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We employ four benchmark datasets: KDDCUP, Thyroid, Arrhythmia, and KDDCUP-Rev. KDDCUP. The KDDCUP99 10 percent dataset from the UCI repository (Lichman (2013)) Thyroid. The Thyroid (Lichman (2013)) dataset is obtained from the ODDS repository 1. 1http://odds.cs.stonybrook.edu/ Arrhythmia. The Arrhythmia (Lichman (2013)) dataset is also obtained from the ODDS repository. |
| Dataset Splits | No | The paper mentions '50% of data by random sampling for training with the rest 50% reserved for testing', but does not specify a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'tensorflow (Abadi et al. (2016))' and 'Adam (Kingma & Ba (2015)) algorithm' but does not specify their version numbers. |
| Experiment Setup | Yes | The network structures of DAGMM used on individual datasets are summarized as follows. KDDCUP. [...] FC(120, 60, tanh)-FC(60, 30, tanh)-FC(30, 10, tanh)-FC(10, 1, none)-FC(1, 10, tanh)-FC(10, 30, tanh)-FC(30, 60, tanh)-FC(60, 120, none), and the estimation network performs with FC(3, 10, tanh)-Drop(0.5)-FC(10, 4, softmax). [...] trained by Adam (Kingma & Ba (2015)) algorithm with learning rate 0.0001. For KDDCUP, Thyroid, Arrhythmia, and KDDCUP-Rev, the number of training epochs are 200, 20000, 10000, and 400, respectively. For the sizes of mini-batches, they are set as 1024, 1024, 128, and 1024, respectively. Moreover, in all the DAGMM instances, we set λ1 as 0.1 and λ2 as 0.005. |