RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection
Authors: Boyang Liu, Ding Wang, Kaixiang Lin, Pang-Ning Tan, Jiayu Zhou
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed extensive experiments to compare the performance of RCA against various baseline methods. The code is available at https://github.com/illidanlab/RCA. For evaluation, we used 18 benchmark datasets obtained from the Stony Brook ODDS library [Rayana, 2016]. We reserve 60% of the data for training and the remaining 40% for testing. The performance of the competing methods are evaluated based on their Area under ROC curve (AUC) scores. |
| Researcher Affiliation | Academia | Boyang Liu1 and Ding Wang1 and Kaixiang Lin1 and Pang-Ning Tan1 and Jiayu Zhou1 1Michigan State University, Department of Computer Science and Engineering {liuboya2, wangdin1, linkaixi, ptan, jiayuz}@msu.edu |
| Pseudocode | Yes | A pseudocode for RCA with k = 2 autoencoders is shown in Algorithm 1. Algorithm 1: Robust Collaborative Autoencoders |
| Open Source Code | Yes | The code is available at https://github.com/illidanlab/RCA. |
| Open Datasets | Yes | For evaluation, we used 18 benchmark datasets obtained from the Stony Brook ODDS library [Rayana, 2016]1. 1Additional experimental results on the CIFAR10 dataset are given in the longer version of the paper. [Rayana, 2016] Shebuti Rayana. ODDS library. http://odds. cs.stonybrook.edu, 2016. Accessed: 2020-09-01. |
| Dataset Splits | No | We reserve 60% of the data for training and the remaining 40% for testing. The paper does not explicitly mention a validation split percentage or size; it only specifies training and testing splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies used in the experiments (e.g., Python, PyTorch, TensorFlow, or specific library versions). |
| Experiment Setup | Yes | Algorithm 1: Robust Collaborative Autoencoders input: training data Xtrn, test data Xtst, anomaly ratio ϵ, dropout rate r, decay rate α, and max epoch for training; To ensure fair comparison, we maintain similar hyperparameter settings for all the competing DNN-based approaches. More discussion about our experimental setting will be given in the long version of the paper. |