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 | Conference PDF | Archive PDF | Plain Text | 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. |