Learning with Multiple Complementary Labels
Authors: Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct extensive experiments to evaluate the performance of our proposed approaches including the two wrappers, the unbiased risk estimator with various loss functions and the two upper-bound surrogate loss functions. Datasets. We use five widely-used benchmark datasets MNIST (Le Cun et al., 1998), Kuzushiji-MNIST (Clanuwat et al., 2018), Fashion-MNIST (Xiao et al., 2017), 20Newsgroups (Lang, 1995), and CIFAR-10 (Krizhevsky et al., 2009), and four datasets from the UCI repository (Blake & Merz, 1998). ... Table 2, Table 3, and Table 4 show the experimental results of different approaches... |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Nanyang Technological University, Singapore 2The University of Tokyo 3RIKEN Center for Advanced Intelligence Project 4Department of Computer Science, Hong Kong Baptist University. |
| Pseudocode | No | No pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper mentions 'We implement our approach using Py Torch1' with a footnote linking to PyTorch's website (www.pytorch.org), which is a third-party library, not the authors' own source code for their methodology. No other statements about open-sourcing their code were found. |
| Open Datasets | Yes | Datasets. We use five widely-used benchmark datasets MNIST (Le Cun et al., 1998), Kuzushiji-MNIST (Clanuwat et al., 2018), Fashion-MNIST (Xiao et al., 2017), 20Newsgroups (Lang, 1995), and CIFAR-10 (Krizhevsky et al., 2009), and four datasets from the UCI repository (Blake & Merz, 1998). |
| Dataset Splits | Yes | Hyperparameters for all the approaches are selected so as to maximize the accuracy on a validation set (10% of the training set) of complementarily labeled data. |
| Hardware Specification | Yes | All the experiments are conducted on NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions 'We implement our approach using Py Torch1' but does not specify a version number for PyTorch or any other software dependencies with their versions. |
| Experiment Setup | Yes | Learning rate and weight decay are selected from t10 6, 10 5, , 10 1u. We implement our approach using Py Torch1, and use the Adam (Kingma & Ba, 2015) optimization method with minibatch size set to 256 and epoch number set to 250. |