AUC Optimization from Multiple Unlabeled Datasets
Authors: Zheng Xie, Yu Liu, Ming Li
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we report the experimental results of the proposed Um-AUC, compared to state-of-the-art Um classification approaches. |
| Researcher Affiliation | Academia | National Key Laboratory for Novel Software Technology, Nanjing University, China School of Artificial Intelligence, Nanjing University, China |
| Pseudocode | Yes | Algorithm 1 Um-AUC |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a direct link to a code repository. |
| Open Datasets | Yes | We tested the performance of Um-AUC using the benchmark datasets Kuzushiji-MNIST (K-MNIST for short) (Clanuwat et al. 2018), CIFAR-10, and CIFAR100 (Krizhevsky, Hinton et al. 2009) |
| Dataset Splits | No | The paper mentions 'training set' and 'test set' but does not explicitly provide details about a validation set or specific training/validation splits. It states: 'We train all models for 150 epochs, and report the AUC on the test set at the final epoch.' |
| Hardware Specification | Yes | Our implementation is based on Py Torch (Paszke et al. 2019), and experiments are conducted on an NVIDIA Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al. 2019)' but does not specify a version number for it or any other software dependencies needed for replication. |
| Experiment Setup | Yes | We train all models for 150 epochs, and report the AUC on the test set at the final epoch. We used Adam (Kingma and Ba 2014) and cross-entropy loss for their optimization, following the standard implementation in the original paper. |