Likelihood-free Out-of-Distribution Detection with Invertible Generative Models
Authors: Amirhossein Ahmadian, Fredrik Lindsten
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The performance results in terms of the Area Under ROC curve (AUROC) are given in Table 1 . We have experimented on some of the image dataset pairs which are common in the literature of OOD detection, such as MNIST vs. Fashion-MNIST, and CIFAR10 vs. SVHN. |
| Researcher Affiliation | Academia | Amirhossein Ahmadian , Fredrik Lindsten Division of Statistics and Machine Learning, Department of Computer and Information Science, Link oping University {amirhossein.ahmadian, fredrik.lindsten}@liu.se |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and supplementary material are available at: https://github.com/aahmadian-liu/ood-likefree-invertible |
| Open Datasets | Yes | We have experimented on some of the image dataset pairs which are common in the literature of OOD detection, such as MNIST vs. Fashion-MNIST, and CIFAR10 vs. SVHN. |
| Dataset Splits | Yes | More specifically, the generative model is (or has been) trained on the entire standard training partition of the in-distribution data; the OSVM model is trained on 3000 random samples from the standard test partition of the in-distribution data, and is tested on 3000 different random samples from the test partition of this dataset in addition to 3000 random samples from the OOD dataset. |
| Hardware Specification | Yes | on a Titan X Pascal GPU with batch size=10. |
| Software Dependencies | No | The paper mentions that the models are 'implemented in Py Torch' but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The settings for this approximation in our experiments can be found in appendix B. [...] on a Titan X Pascal GPU with batch size=10. |