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..
LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning
Authors: Atsuyuki Miyai, Qing Yu, Go Irie, Kiyoharu Aizawa
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the large-scale Image Net OOD detection benchmarks demonstrate the superiority of our Lo Co Op over zero-shot, fully supervised detection methods and prompt learning methods. |
| Researcher Affiliation | Collaboration | Atsuyuki Miyai1 Qing Yu1,2 Go Irie3 Kiyoharu Aizawa1 1The University of Tokyo 2LY Corporation 3Tokyo University of Science |
| Pseudocode | No | The paper includes diagrams and descriptions of the method but no formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available via https: //github.com/Atsu Miyai/Lo Co Op. |
| Open Datasets | Yes | Datasets. We use the Image Net-1K dataset [5] as the ID data. For OOD datasets, we adopt the same ones as in [18], including subsets of i Naturalist [47], SUN [51], Places [58], and TEXTURE [4]. |
| Dataset Splits | Yes | For the few-shot training, we follow the few-shot evaluation protocol adopted in CLIP [37] and Co Op [60], using 1, 2, 4, 8, and 16 shots for training, respectively, and deploying models in the full test sets. |
| Hardware Specification | Yes | We use a single Nvidia A100 GPU for all experiments. |
| Software Dependencies | No | The paper mentions using 'CLIP-Vi T-B/16 models' and refers to CLIP, but it does not list specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | Other hyperparameters (e.g., training epoch=50, learning rate=0.002, batch size=32, and token lengths N=16) are the same as those of Co Op [60]. |