ReAct: Out-of-distribution Detection With Rectified Activations
Authors: Yiyou Sun, Chuan Guo, Yixuan Li
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive evaluations and establish state-of-the-art performance on a suite of common OOD detection benchmarks, including CIFAR-10 and CIFAR-100, as well as a large-scale Image Net dataset [7]. Re Act outperforms the best baseline by a large margin, reducing the average FPR95 by up to 25.05%. |
| Researcher Affiliation | Collaboration | Yiyou Sun Department of Computer Sciences University of Wisconsin-Madison sunyiyou@cs.wisc.edu Chuan Guo Facebook AI Research chuanguo@fb.com Yixuan Li Department of Computer Sciences University of Wisconsin-Madison sharonli@cs.wisc.edu |
| Pseudocode | No | The paper describes the Re Act operation mathematically (equations 1 and 2) but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/deeplearning-wisc/react.git |
| Open Datasets | Yes | We use a pre-trained Res Net-50 model [12] for Image Net-1k. ... We evaluate on CIFAR-10 and CIFAR-100 [27] datasets as in-distribution data, using the standard split with 50,000 training images and 10,000 test images. |
| Dataset Splits | No | The paper mentions 'We use a validation set of Gaussian noise images' for selecting the parameter 'p', but does not provide specific dataset split information (percentages, sample counts) for the main in-distribution datasets (ImageNet, CIFAR-10/100) to create a validation set. |
| Hardware Specification | No | The paper states 'All experiments are based on the hardware described in Appendix D.' However, Appendix D is not provided in the given text, thus specific hardware details are not available in the main body. |
| Software Dependencies | No | The paper mentions models like ResNet-50 and MobileNet-v2, and concepts like Batch Norm, Weight Norm, and Group Norm, but it does not specify any software dependencies (e.g., libraries, frameworks) with version numbers needed for replication. |
| Experiment Setup | Yes | We select p from {10, 65, 80, 85, 90, 95, 99} based on the FPR95 performance. The optimal p is 90. ... For both CIFAR-10 and CIFAR-100, the models are trained for 100 epochs. The start learning rate is 0.1 and decays by a factor of 10 at epochs 50, 75, and 90. |