Understanding the Effect of Bias in Deep Anomaly Detection
Authors: Ziyu Ye, Yuxin Chen, Haitao Zheng
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We establish the first finite sample rates for estimating the relative scoring bias for deep anomaly detection, and empirically validate our theoretical results on both synthetic and real-world datasets. We also provide an extensive empirical study on how a biased training anomaly set affects the anomaly score function and therefore the detection performance on different anomaly classes. |
| Researcher Affiliation | Academia | Ziyu Ye , Yuxin Chen and Haitao Zheng University of Chicago {ziyuye, chenyuxin}@uchicago.edu, htzheng@cs.uchicago.edu |
| Pseudocode | Yes | Algorithm 1: Computing the anomaly detection threshold for Problem 3.2 |
| Open Source Code | No | The paper does not provide a link to its source code or explicitly state that the code is open-source. |
| Open Datasets | Yes | We empirically validate our assumptions and theoretical results on both synthetic and three real-world datasets4 (Fashion-MNIST, Stat Log (Landsat Satellite), and Cellular Spectrum Misuse [Li et al., 2019]). |
| Dataset Splits | Yes | To set the threshold, we fix the target FPR to be 0.05, and vary the number of normal data in the validation set n from {100, 1K, 10K}. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | Detailed descriptions on datasets and training configurations are listed in Appendix C. (Appendix C is not provided in the paper text). |