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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE
Authors: Yewen Li, Chaojie Wang, Xiaobo Xia, Tongliang Liu, xin miao, Bo An
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our method can significantly outperform existing state-of-the-art unsupervised OOD detection approaches. 4 Experiments |
| Researcher Affiliation | Collaboration | 1Nanyang Technological University 2University of Sydney 3Amazon |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See supplemental material |
| Open Datasets | Yes | Fashion MNIST [39] (in) / MNIST [40] (out) and CIFAR10 [41] (in) / SVHN [42] (out)... we add KMNIST [43], not MNIST [44], Omniglot [45] and Small NORB [46] datasets; for CIFAR10/SVHN pair, we add Celeb A [47], Places365 [48], Flower102 [49] and LFWPeople [50] datasets. |
| Dataset Splits | No | The paper mentions "trained on the training split" and "evaluated on both the testing split" but does not explicitly provide details about a validation dataset split or its size/percentage for their own experiments. |
| Hardware Specification | Yes | All experiments are performed on a PC with an NVIDIA RTX 3090 GPU and the our code is implemented with Py Torch [53]. |
| Software Dependencies | Yes | The models are implemented in PyTorch 1.10.1. |
| Experiment Setup | Yes | For optimization, we adopt the same Adam optimizer [52] with a learning rate of 3e-4. We train all models in comparison by setting the batch size as 128 and the max epoch as 1000. |