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..
Learning Bounds for Open-Set Learning
Authors: Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments verify the efficacy of AOSR. Experiments on real datasets indicate that AOSR achieves competitive performance when compared with baselines. |
| Researcher Affiliation | Academia | 1AAII, University of Technology Sydney. |
| Pseudocode | Yes | Section 4. A Principle Guided OSL Algorithm: Step 1 (Feature Encoding). Step 2 (Initialize the Auxiliary Domain). Step 3 (Construct the Auxiliary Domain). Step 4 (Softmax C+1). Step 5 (Open-set Learning). |
| Open Source Code | Yes | The code is available at github.com/ Anjin-Liu/Openset_Learning_AOSR. |
| Open Datasets | Yes | Double-moon dataset, MNIST (Le Cun & Cortes, 2010), Omniglot (Ager, 2008), CIFAR-10 (Krizhevsky & Hinton, 2009), SVHN (Netzer et al., 2011), CIFAR100 (Krizhevsky & Hinton, 2009). |
| Dataset Splits | Yes | Following the set up in Yoshihashi et al. (2019), we use MNIST (Le Cun & Cortes, 2010) as the training samples and use Omniglot (Ager, 2008), MNIST-Noise, and Noise (Liu et al., 2021) datasets as unknown classes. We implement double-moon dataset with varying size n 2. We also generate n test samples. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory amounts used for running experiments. |
| Software Dependencies | No | The paper does not list specific versions of software dependencies or libraries used for the experiments. |
| Experiment Setup | Yes | AOSR has several hyper-parameters: β, t, µ and m. For all tasks, we set m = 3n, t = 10% as default. µ is a dynamic parameter depending on β: µ = nβ n +0.0001, where n is number of samples in training samples actually predicted as unknown. For example, if β = 0.05, n is 1000, there are 10 samples in training samples are predicted as unknown, then µ 5. |