Learning Bounds for Open-Set Learning
Authors: Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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. |