Robust Subspace Recovery Layer for Unsupervised Anomaly Detection
Authors: Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical experiments with both image and document datasets demonstrate state-of-the-art precision and recall. and 4 EXPERIMENTAL RESULTS We test our method 1on five datasets: Caltech 101 (Fei-Fei et al., 2007), Fashion-MNIST (Xiao et al., 2017), Tiny Imagenet (a small subset of Imagenet (Russakovsky et al., 2015)), Reuters-21578 (Lewis, 1997) and 20 Newsgroups (Lang, 1995). |
| Researcher Affiliation | Academia | Chieh-Hsin Lai , Dongmian Zou & Gilad Lerman School of Mathematics University of Minnesota Minneapolis, MN 55455 {laixx313, dzou, lerman}@umn.edu |
| Pseudocode | Yes | Algorithm 1 RSRAE and Algorithm 2 RSRAE+ in Appendix A. |
| Open Source Code | Yes | Our implementation is available at https://github.com/dmzou/RSRAE.git (footnote 1 in Section 4). |
| Open Datasets | Yes | We test our method 1on five datasets: Caltech 101 (Fei-Fei et al., 2007), Fashion-MNIST (Xiao et al., 2017), Tiny Imagenet (a small subset of Imagenet (Russakovsky et al., 2015)), Reuters-21578 (Lewis, 1997) and 20 Newsgroups (Lang, 1995). |
| Dataset Splits | No | The paper describes how inliers and outliers are sampled for experiments, but does not specify a separate validation dataset split for model training or hyperparameter tuning. For example, for Fashion-MNIST, it states: 'We use the test set which contains 10,000 images and normalize pixel values to lie in [-1, 1]. In each experiment, we fix a class and the inliers are the test images in this class.' |
| Hardware Specification | Yes | All experiments were executed on a Linux machine with 64GB RAM and four GTX1080Ti GPUs. |
| Software Dependencies | No | For all experiments with neural networks, we used Tensor Flow and Keras. The LOF, OCSVM and IF methods are adapted from the scikit-learn packages. No version numbers are provided for these software components. |
| Experiment Setup | Yes | We describe the structure of the RSRAE as follows. For the image datasets without deep features, the encoder consists of three convolutional layers: 5 5 kernels with 32 output channels, strides 2; ... For each experiment, the RSRAE model is optimized with Adam using a learning rate of 0.00025 and 200 epochs. The batch size is 128 for each gradient step. |