Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning
Authors: Wei Tang, Weijia Zhang, Min-Ling Zhang
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results obtained from benchmark and real-world datasets demonstrate the superiority of the proposed ELIMIPL over existing MIPL algorithms and other well-established partial-label learning algorithms. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2Key Lab. of Computer Network and Information Integration (Southeast University), Mo E, China 3School of Information and Physical Sciences, The University of Newcastle, NSW 2308, Australia |
| Pseudocode | Yes | Algorithm 1 Training Procedure of ELIMIPL |
| Open Source Code | Yes | The code of ELIMIPL can be found at https://github.com/tangw-seu/ELIMIPL. |
| Open Datasets | Yes | We employ four benchmark MIPL datasets [Tang et al., 2024; Tang et al., 2023]: MNIST-MIPL, FMNIST-MIPL, Birdsong-MIPL, and SIVAL-MIPL, spanning diverse domains such as image analysis and biology [Le Cun et al., 1998; Xiao et al., 2017; Briggs et al., 2012; Settles et al., 2007]. The characteristics of the datasets are presented in Table 1 |
| Dataset Splits | No | The paper mentions "train/test splits" but does not explicitly describe a separate validation split or how validation was performed if integrated (e.g., within the training split or via cross-validation). |
| Hardware Specification | Yes | We implement ELIMIPL using Py Torch and execute it on a single NVIDIA Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions using "PyTorch" but does not specify its version number or versions of any other software dependencies needed to replicate the experiment. |
| Experiment Setup | Yes | We utilize the stochastic gradient descent (SGD) optimizer with a momentum value of 0.9 and a weight decay of 0.0001. The initial learning rate is selected from the set {0.01, 0.05} and accompanied by a cosine annealing technique. We set the number of epochs uniformly to 100 for all datasets. For the MNIST-MIPL and FMNIST-MIPL datasets, µ is set to 1 or 0.1, γ is chosen from {0.1, 0.5}, and the feature extraction network ψ1( ) is a two-layer convolutional neural network. For the remaining datasets, we set both µ and γ to 10, and ψ1( ) is an identity transformation. The feature transformation network ψ2( ) is implemented by a fully connected network, with the dimension l set to 512 for the CRC-MIPL dataset and 128 for the other datasets. |