Robust Self-Supervised Multi-Instance Learning with Structure Awareness
Authors: Yejiang Wang, Yuhai Zhao, Zhengkui Wang, Meixia Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Compared with state-of-the-art supervised MIL baselines, SMILES achieves average improvement of 4.9%, 4.4% in classification accuracy on 5 benchmark datasets and 20 newsgroups datasets, respectively. In addition, we show that the model is robust to the input corruption. |
| Researcher Affiliation | Academia | Yejiang Wang1, Yuhai Zhao1, Zhengkui Wang2, Meixia Wang1 1School of Computer Science and Engineering, Northeastern University, China 2Info Comm Technology Cluster, Singapore Institute of Technology, Singapore wangyejiang@stumail.neu.edu.cn, zhaoyuhai@mail.neu.edu.cn, zhengkui.wang@singaporetech.edu.sg, wangmeixia@stumail.neu.edu.cn |
| Pseudocode | Yes | Algorithm 1: SMILES. 1: input: unlabeled training data X X, batch size N, temperature τ, augmentation ratio c, encoder network frep, pre-train head network g. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | We empirically evaluate SMILES against state-of-the-art supervised multi-instance learning algorithms on five popular benchmark datasets, twenty text datasets from the 20Newsgroups corpus and three datasets for the task of biocreative text categorization (see the Appendix for detail). |
| Dataset Splits | Yes | For SMILES, we report the mean 10-fold cross validation accuracy after 5 runs followed by a linear SVM. The linear SVM is trained by applying cross validation on training data folds and the best mean accuracy is reported. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software components like 'linear SVM' and implicitly 'neural networks', but it does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We conduct experiment with the values of the number of message passing layers, the number of epochs, batch size, the parameter C of SVM, the threshold ϵ, augmentation ratio c and temperature τ in the sets {2, 4, 8, 12}, {10, 20, 40, 100}, {32, 64, 128, 256}, {10 3, . . . , 102, 103}, {0.1, . . . , 0.5}, {10%, . . . , 50%} and {0.05, 0.1, 0.2, 0.5, 1.0, 2.0} respectively. The hidden dimension of layer is set to 128. |