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..
Delving into Out-of-Distribution Detection with Vision-Language Representations
Authors: Yifei Ming, Ziyang Cai, Jiuxiang Gu, Yiyou Sun, Wei Li, Yixuan Li
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that MCM achieves superior performance on a wide variety of real-world tasks. |
| Researcher Affiliation | Collaboration | Yifei Ming1 Ziyang Cai1 Jiuxiang Gu2 Yiyou Sun1 Wei Li3 Yixuan Li1 1Department of Computer Sciences, University of Wisconsin-Madison 2Adobe 3Google Research |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/ deeplearning-wisc/MCM. |
| Open Datasets | Yes | We consider the following ID datasets: CUB-200 [80], STANFORD-CARS [39], FOOD-101 [6], OXFORD-PET [57] and variants of IMAGENET [11]. |
| Dataset Splits | No | The paper states 'λ is chosen so that a high fraction of ID data (e.g., 95%) is above the threshold' which describes a validation-like process for threshold selection, but it does not specify a formal dataset split (e.g., percentages or sample counts) for this validation. |
| Hardware Specification | No | The paper mentions models like CLIP-B/16 and Vi T-B/16, but does not specify the hardware (e.g., GPU/CPU models, memory) used to run the experiments within the provided text. |
| Software Dependencies | No | The paper mentions using 'CLIP' and 'Transformer' models but does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | Unless specified otherwise, the temperature is 1 for all experiments. |