Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection
Authors: Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman
ICLR 2020 | Venue PDF | 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 EMAIL |
| 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. |