Transfer Anomaly Detection by Inferring Latent Domain Representations
Authors: Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of the proposed method is demonstrated through experiments using one synthetic and four real-world datasets. |
| Researcher Affiliation | Industry | Atsutoshi Kumagai NTT Software Innovation Center NTT Secure Platform Laboratories atsutoshi.kumagai.ht@hco.ntt.co.jp Tomoharu Iwata NTT Communication Science Laboratories tomoharu.iwata.gy@hco.ntt.co.jp Yasuhiro Fujiwara NTT Communication Science Laboratories yasuhiro.fujiwara.kh@hco.ntt.co.jp |
| Pseudocode | No | The paper describes the proposed method and equations but does not include a formal pseudocode or algorithm block. |
| Open Source Code | No | The proposed method was implemented by Chainer [45]. |
| Open Datasets | Yes | We used four real-world public datasets: MNIST-r, Anuran Calls, Landmine, and Io T. ... Due to the length limit of the paper, the details of the datasets including download links are provided in the supplemental material. |
| Dataset Splits | No | We selected hyperparameters using average validation AUC on the source domains for all methods except for AE. |
| Hardware Specification | Yes | We used the following setup: CPU was Intel Xeon E5-2660v3 2.6 GHz, the memory size was 128 GB, and GPU was NVIDIA Tesla k80. |
| Software Dependencies | No | The proposed method was implemented by Chainer [45]. |
| Experiment Setup | Yes | We set the hyperparameters as follows: the regularization parameter of the AUC loss λ was 104, the dimension of the latent domain vector K was 20, the regularization parameter of the latent domain vector β was one, and the sample size of the reparametrization trick L was one. |