VOS: Learning What You Don't Know by Virtual Outlier Synthesis
Authors: Xuefeng Du, Zhaoning Wang, Mu Cai, Yixuan Li
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | VOS achieves competitive performance on both object detection and image classification models, reducing the FPR95 by up to 9.36% compared to the previous best method on object detectors. Code is available at https://github.com/deeplearning-wisc/vos. |
| Researcher Affiliation | Academia | Xuefeng Du, Zhaoning Wang, Mu Cai, Yixuan Li Department of Computer Sciences University of Wisconsin Madison {xfdu,mucai,sharonli}@cs.wisc.edu |
| Pseudocode | Yes | Algorithm 1 VOS: Virtual Outlier Synthesis for OOD detection |
| Open Source Code | Yes | Code is available at https://github.com/deeplearning-wisc/vos. |
| Open Datasets | Yes | We use PASCAL VOC (Everingham et al., 2010) and Berkeley Deep Drive (BDD-100k) (Yu et al., 2020) datasets as the ID training data. For both tasks, we evaluate on two OOD datasets that contain subset of images from: MS-COCO (Lin et al., 2014) and Open Images (validation set) (Kuznetsova et al., 2020). |
| Dataset Splits | Yes | We use CIFAR-10 (Krizhevsky & Hinton, 2009) as the ID training data, with standard train/val splits. ID val dataset VOC val BDD val |
| Hardware Specification | Yes | We run all experiments with Python 3.8.5 and Py Torch 1.7.0, using NVIDIA Ge Force RTX 2080Ti GPUs. |
| Software Dependencies | Yes | We run all experiments with Python 3.8.5 and Py Torch 1.7.0, using NVIDIA Ge Force RTX 2080Ti GPUs. |
| Experiment Setup | Yes | We use the Detectron2 library (Girshick et al., 2018) and train on two backbone architectures: Res Net-50 (He et al., 2016) and Reg Net X-4.0GF (Radosavovic et al., 2020). We employ a two-layer MLP with a Re LU nonlinearity for φ in Equation 5, with hidden layer dimension of 512. For each in-distribution class, we use 1,000 samples to estimate the class-conditional Gaussians. [...] The PASCAL model is trained for a total of 18,000 iterations, and the BDD-100k model is trained for 90,000 iterations. We add the uncertainty regularizer (Equation 5) starting from 2/3 of the training. The weight β is set to 0.1. |