Gradient-Based Novelty Detection Boosted by Self-Supervised Binary Classification
Authors: Jingbo Sun, Li Yang, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar, Deliang Fan, Yu Cao8370-8377
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the evaluation with multiple datasets, such as CIFAR-10, CIFAR-100, SVHN and Tiny Image Net, the proposed approach consistently outperforms state-of-the-art supervised and unsupervised methods in the area under the receiver operating characteristic (AUROC) and area under the precision-recall curve (AUPR) metrics. |
| Researcher Affiliation | Collaboration | 1 Arizona State University 2 Oak Ridge National Laboratory 3 Pacific Northwest National Laboratory |
| Pseudocode | Yes | Algorithm 1: Gradient-based Novelty Detection Boosted by Self-supervised Binary Classification |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the availability of open-source code for the methodology described. |
| Open Datasets | Yes | Our experiment includes three in-distribution datasets: CIFAR-10, CIFAR-100 and SVHN. We test each of them using the other two as the OOD. In addition, we use two more OOD datasets: the resized version of the Image Net (Russakovsky et al. 2015) and LSUN (Yu et al. 2016) provided by (Liang, Li, and Srikant 2017). |
| Dataset Splits | Yes | For each IDD/OOD pair, we randomly select 5,000/5,000 (IDD/OOD) as the training dataset and further divide them into mini batches with size of 100/100 (IDD/OOD) to emulate the data streaming, the rest 5,000/5,000 (IDD/OOD) samples are used to test the binary classifier accuracy and the overall novelty detection performance after each new batch has been taken into the system. |
| Hardware Specification | Yes | All experiments are performed with Py Torch (Paszke et al. 2019) on one NVIDIA Ge Force RTX 2080 platform. |
| Software Dependencies | No | The paper mentions "Py Torch (Paszke et al. 2019)" but does not specify a version number for this or any other software dependency. |
| Experiment Setup | Yes | Our simple binary classifier has the structure of three convolution layers and one batch normalization layer with a Sigmoid classifier. It is trained by minimizing the cross-entropy loss using Adam (Kingma and Ba 2014). The initial learning rate is set to 0.0002 and the decay rate is controlled by β1 = 0.5 and β2 = 0.999. We train it for 500 epochs in both initial training and re-training process. |