Mean-Shifted Contrastive Loss for Anomaly Detection
Authors: Tal Reiss, Yedid Hoshen
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrating the state-of-the-art anomaly detection performance of our method (e.g. 98.6% ROC-AUC on CIFAR-10). |
| Researcher Affiliation | Academia | Tal Reiss, Yedid Hoshen The Hebrew University of Jerusalem |
| Pseudocode | No | The paper contains mathematical equations but does not include explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present structured steps in a code-like format. |
| Open Source Code | Yes | Full training and implementation details are in the Supplementary Material1. 1https://github.com/talreiss/Mean-Shifted-Anomaly-Detection |
| Open Datasets | Yes | We evaluated our approach on a wide range of anomaly detection benchmarks. Following (Golan and El-Yaniv 2018; Hendrycks et al. 2019), we run our experiments on commonly used datasets: CIFAR-10 (Krizhevsky, Hinton et al. 2009), CIFAR-100 coarse-grained version that consists of 20 classes (Krizhevsky, Hinton et al. 2009), and Cats Vs Dogs (Elson et al. 2007). |
| Dataset Splits | No | The paper mentions 'training set of normal samples' and 'normal training images' but does not explicitly provide specific percentages, sample counts, or methodology for train/validation/test splits for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or cloud computing instances used for running the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) that would be needed to replicate the experiments. |
| Experiment Setup | No | The paper mentions fine-tuning with Lmsc and using ResNet152 but states 'Full training and implementation details are in the Supplementary Material' without providing specific hyperparameters such as learning rate, batch size, or number of epochs within the main text. |