Anomaly Detection With Multiple-Hypotheses Predictions
Authors: Duc Tam Nguyen, Zhongyu Lou, Michael Klar, Thomas Brox
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the anomaly detection performance of our approach on CIFAR-10 and a real anomaly image dataset, the Metal Anomaly dataset... We show that anomaly detection performance with multiple-hypotheses networks is significantly better compared to single-hypotheses networks. On CIFAR-10, our proposed Con AD framework (consistency-based anomaly detection) improves on previously published results. |
| Researcher Affiliation | Collaboration | 1Computer Vision Group, University of Freiburg, Freiburg, Germany 2Corporate Research, Robert Bosch Gmb H, Renningen, Germany. |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We evaluate the anomaly detection performance of our approach on CIFAR-10 and a real anomaly image dataset, the Metal Anomaly dataset |
| Dataset Splits | Yes | Table 1. Dataset description. ... NORMAL DATA TRAIN 4500 5408 VALID 500 1352 TEST 1000 1324 |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like 'Adam' and 'DCGAN' but does not specify version numbers for any key software components or libraries. |
| Experiment Setup | Yes | The batch-size n was set to 64 each on CIFAR-10, 32 on the Metal Anomaly dataset. Adam (Kingma & Ba, 2014) was used for training with a learning rate of 0.001. Per discriminator training, the generator is trained at most five epochs to balance both players. |