Fast Decision Boundary based Out-of-Distribution Detector
Authors: Litian Liu, Yao Qin
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method matches or surpasses the effectiveness of state-of-the-art methods in extensive experiments while incurring negligible overhead in inference latency. Overall, our approach significantly improves the efficiency-effectiveness trade-off in OOD detection. ... Experimental analysis: In Section 4, we demonstrate across extensive experiments that f DBD achieves or surpasses the state-of-the-art OOD detection effectiveness with negligible latency overhead. |
| Researcher Affiliation | Academia | 1MIT 2UC Santa Barbara. Correspondence to: Litian Liu <litianl@mit.edu>, Yao Qin <yaoqin@ucsb.edu>. |
| Pseudocode | No | The paper describes the proposed method and provides theoretical proofs but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https: //github.com/litianliu/f DBD-OOD. |
| Open Datasets | Yes | On the CIFAR-10 OOD benchmark, we use the standard CIFAR-10 test set with 10,000 images as ID test samples. For OOD samples, we consider common OOD benchmarks: SVHN (Netzer et al., 2011), i SUN (Xu et al., 2015), Places365 (Zhou et al., 2017), and Texture (Cimpoi et al., 2014). |
| Dataset Splits | Yes | Datasets On the CIFAR-10 OOD benchmark, we use the standard CIFAR-10 test set with 10,000 images as ID test samples. ... We use 50,000 Image Net validation images in the standard split as ID test samples. |
| Hardware Specification | Yes | In particular, on a Tesla T4 GPU, the average inference time on the CIFAR-10 classifier is 0.53ms per image with or without computing the distance using our method. ... On a Tesla T4 GPU, estimating the distance using CW attack takes 992.2ms per image per class. |
| Software Dependencies | No | The paper mentions using 'Pytorch' and refers to a 'training recipe' link for Res Net-50 models, but it does not explicitly state specific version numbers for PyTorch or any other software libraries used. |
| Experiment Setup | Yes | Res Net-18 w/ Cross Entropy Loss... The classifier is trained for 100 epochs, with a start learning rate 0.1 decaying to 0.01, 0.001, and 0.0001 at epochs 50, 75, and 90 respectively. ... Res Net-18 w/ Contrastive Loss... the model is trained with for 500 epochs with batch size 1024. The temperature is set to 0.1. The cosine learning rate (Loshchilov & Hutter, 2016) starts at 0.5 is used. |