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..
OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification
Authors: Taewon Jeong, Heeyoung Kim
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run the experiments for few-shot OOD detection and classification tasks with OOD-MAML. In the meta-training phase, we set the 5-shot data of one class in Dtrain and set 50 samples in Dtest... Under these settings, we evaluated the performance of OOD-MAML by implementing OOD detection and classification in experiments, and compared the obtained results with the performances of several OOD detection methods. |
| Researcher Affiliation | Academia | Taewon Jeong Heeyoung Kim Department of Industrial and Systems Engineering KAIST Daejeon 34141, Republic of Korea EMAIL |
| Pseudocode | No | The paper describes methods using mathematical equations and textual explanations, but it does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | The code for OOD-MAML is available at https://github.com/twj-KAIST/OOD-MAML. |
| Open Datasets | Yes | We ran experiments on Omniglot [14], CIFAR-FS [2], and mini Image Net [24], which are popular benchmark datasets used for few-shot learning. |
| Dataset Splits | No | The paper describes task-specific data usage for meta-training and meta-testing (e.g., 'In the meta-training phase, we set the 5-shot data of one class in Dtrain and set 50 samples in Dtest...') but does not provide explicit train/validation/test dataset splits for a single, overall dataset. The word 'validation' is used once in the context of validating adaptation, not a dataset split. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies, libraries, or frameworks used in the experiments. |
| Experiment Setup | No | The paper mentions general aspects of the experimental setup such as '5-shot' and 'N-way' settings, and CNN architecture details like '64 filters for Omniglot and CIFAR-FS, and 32 filters for mini Image Net'. It also mentions learning rates (α, β, βfake). However, it defers specific hyperparameter values to supplementary material: 'Details about hyperparameters are described in Supplementary material.' |