Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Likelihood-free Out-of-Distribution Detection with Invertible Generative Models
Authors: Amirhossein Ahmadian, Fredrik Lindsten
IJCAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 15,970 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,776 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL |
| 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. |