Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection
Authors: Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan O Arik, Chen-Yu Lee, Tomas Pfister
TMLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments across various datasets from different domains, including semantic AD (CIFAR-10 (Krizhevsky & Hinton, 2009), Dog-vs-Cat (Elson et al., 2007)), real-world manufacturing visual AD use case (MVTec (Bergmann et al., 2019)), and real-world tabular AD benchmarks (e.g., detecting medical or network anomalies). We evaluate models at different anomaly ratios of unlabeled training data and show that SRR significantly boosts performance. |
| Researcher Affiliation | Industry | EMAIL Google Cloud AI |
| Pseudocode | Yes | Algorithm 1 SRR: Self-supervise, Refine, Repeat. Input: Train data D = {xi}N i=1, Ensemble count (K), threshold (γ) Output: Refined data ( ˆD), trained OCC (f), feature extractor (g) |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code for the methodology described, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | We conduct extensive experiments across various datasets from different domains, including semantic AD (CIFAR-10 (Krizhevsky & Hinton, 2009), Dog-vs-Cat (Elson et al., 2007)), real-world manufacturing visual AD use case (MVTec (Bergmann et al., 2019)), and real-world tabular AD benchmarks (e.g., detecting medical or network anomalies). Following (Zong et al., 2018; Bergman & Hoshen, 2019), we test the performance of SRR on a variety of real-world tabular AD datasets, including network (KDDCup) and medical (Thyroid, Arrhythmia) AD from the UCI repository (Asuncion & Newman, 2007). |
| Dataset Splits | Yes | To construct the data splits, we utilize 50% of normal samples for training. In addition, we hold out some anomaly samples (amounting to 10% of the normal samples) from the data. This allows to simulate unsupervised settings with an anomaly ratio of up to 10% of entire training set. Rest of the data is used for testing. For MVTec, since there are no anomalous data available for training, we borrow 10% of the anomalies from the test set and swap them with normal samples in the training set. |
| Hardware Specification | Yes | Each experimental run is performed on a single V100 GPU. |
| Software Dependencies | No | The paper mentions using specific models (e.g., Res Net-18 architecture) and optimizers (Momentum SGD) but does not provide specific version numbers for software libraries or programming languages. |
| Experiment Setup | Yes | The same model and hyperparameter configurations are used for SRR with K = 5 classifiers in the ensemble. We set γ as twice the anomaly ratio of training data. For 0% anomaly ratio, we set γ as 0.5. Finally, a Gaussian Density Estimator (GDE) on learned representations is used as the OCC. Optimizer Momentum SGD (momentum= 0.9) Learning rate 0.001 Batch size 64 M L2 weight regularization 0.00003 Random projection dimension 32 |