Adversarial Partial Multi-Label Learning with Label Disambiguation
Authors: Yan Yan, Yuhong Guo10568-10576
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on both synthetic and real-world partial multi-label datasets, while the proposed model demonstrates the state-of-the-art performance. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Engineering, Northwestern Polytechnical University, China 2 School of Computer Science, Carleton University, Canada |
| Pseudocode | Yes | Algorithm 1 Minibatch based stochastic gradient descent training algorithm for PML-GAN |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We conducted experiments on twelve multi-label classification datasets. Three of them have existing partial multi-label learning settings (mirflickr, music style and music emotion (Fang and Zhang 2019)). For each of the other nine datasets (Zhang and Zhou 2014), we transformed it into a PML dataset by randomly adding irrelevant labels into the candidate label set of each training instance. |
| Dataset Splits | No | For each dataset, we randomly select 80% of the data for training and use the remaining 20% for testing. There is no explicit mention of a separate validation split or its size. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions using the Adam optimizer and designing networks as multilayer perceptrons, but does not specify any software dependencies (e.g., programming languages, libraries, frameworks) with version numbers. |
| Experiment Setup | Yes | The mini-batch size, m, is set to 64. The hyperparameters k (the number of steps for discriminator update) and n (the number of label vectors sampled) in Algorithm 1 are set to 1 and 210 respectively. The hyperparameter β is chosen from {0.001, 0.01, 0.1, 1, 10} based on the classification loss value Lc in the training objective function; that is, the β value that leads to the smallest training Lc loss will be chosen. |