Perturbation Learning Based Anomaly Detection
Authors: Jinyu Cai, Jicong Fan
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies on benchmark datasets verify the effectiveness and superiority of our method. In this section, we evaluate the proposed method in comparison to several state-of-the-art anomaly detection methods on two image datasets and two tabular datasets. |
| Researcher Affiliation | Academia | 1Fuzhou University, Fuzhou, China 2Shenzhen Research Institute of Big Data, Shenzhen, China 3The Chinese University of Hong Kong, Shenzhen, China |
| Pseudocode | No | The paper describes the mathematical formulation of the problem and general network architecture, but does not include any pseudocode or formal algorithm blocks. |
| Open Source Code | No | The paper's checklist indicates code is included in supplemental material or URL, but the main text does not provide a direct link or explicit statement about the availability of the source code for the described methodology. |
| Open Datasets | Yes | CIFAR-10: CIFAR-10 image dataset is composed of 60,000 images in total, where 50,000 samples for training and 10,000 samples for test. Fashion-MNIST: Fashion MNIST contains 10 different categories of grey-scale fashion style objects. The data is split into 60,000 images for training and 10,000 images for test. Thyroid: Thyroid is a hypothyroid disease dataset... We follow the data split settings of [Zong et al., 2018] to preprocess the data... Arrhythmia: Arrhythmia dataset... Here we also follow the data split settings of [Zong et al., 2018] to preprocess the data. |
| Dataset Splits | No | The paper specifies training and test samples for datasets like CIFAR-10 and Fashion-MNIST, and refers to external papers for data split settings for others, but it does not explicitly state the proportions or counts for a validation set within its main text. |
| Hardware Specification | Yes | Note that we run all experiments on NVIDIA RTX3080 GPU with 32GB RAM, CUDA 11.0 and cu DNN 8.0. |
| Software Dependencies | Yes | CUDA 11.0 and cu DNN 8.0. |
| Experiment Setup | Yes | For image datasets (CIFAR-10 and Fashion-MNIST), we utilize the Le Net-based CNN to construct the classifier... And we apply the MLP-based VAE to learn the noise for data. For tabular datasets (Thyroid and Arrhythmia), we both use the MLP-based classifier and VAE in practice, and we train them by Adam optimizer with learning rate 0.001. Besides, λ is set to 3 for Thyroid and 2 for Arrhythmia. |