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..
Unknown-Aware Graph Regularization for Robust Semi-supervised Learning from Uncurated Data
Authors: Heejo Kong, Suneung Kim, Ho-Joong Kim, Seong-Whan Lee
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that UAG surpasses state-of-the-art OSSL methods by a large margin across various protocols. Codes are available at https://github.com/heejokong/UAGreg. |
| Researcher Affiliation | Academia | 1Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea 2Department of Artificial Intelligence, Korea University, Seoul, South Korea |
| Pseudocode | No | The paper describes its approach and formulations in text and equations, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes are available at https://github.com/heejokong/UAGreg. |
| Open Datasets | Yes | We conduct extensive experiments on CIFAR-10/100 (Krizhevsky, Hinton et al. 2009) and Image Net-30 (Hendrycks et al. 2019) datasets, following the previous benchmarks. |
| Dataset Splits | Yes | For CIFAR-10 (CIFAR-100), we divide classes into 6 known (60 known) and 4 unknown (40 unknown) classes. All samples, except for the labeled data, are designated as unlabeled data. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU model, CPU model, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions software frameworks like 'WRN-28' and 'Res Net-18' which are model architectures, but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For an outlier detector, T and m are set to 1.5 and 0.9, respectively. |