Provable Guarantees for Understanding Out-of-Distribution Detection
Authors: Peyman Morteza, Yixuan Li7831-7840
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations demonstrate the competitive performance of the new scoring function. In particular, on CIFAR-100 as in-distribution data, GEM outperforms (Liu et al. 2020) by 16.57% (FPR95). |
| Researcher Affiliation | Academia | University of Wisconsin-Madison {peyman, sharonli}@cs.wisc.edu |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/PeymanMorteza/GEM |
| Open Datasets | Yes | We use CIFAR-10 and CIFAR-100 (Krizhevsky, Hinton et al. 2009) datasets as in-distribution data. |
| Dataset Splits | Yes | We use the standard split, and train with Wide ResNet architecture (Zagoruyko and Komodakis 2016) with depth 40. |
| Hardware Specification | No | The paper does not specify any details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions using 'Wide ResNet architecture' but does not specify software dependencies with version numbers (e.g., deep learning framework and its version). |
| Experiment Setup | No | The paper states training with 'Wide Res Net architecture with depth 40' but lacks specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed optimizer settings. |