Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning
Authors: Dong-Dong Wu, Deng-Bao Wang, Min-Ling Zhang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments and analysis on multiple datasets validate the rationality and superiority of our proposed approach. |
| Researcher Affiliation | Academia | School of Computer Science and Engineering, Southeast University, Nanjing 210096, China Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China EMAIL |
| Pseudocode | Yes | The pseudo-code of our complete algorithm DIRK is shown in Appendix A.3. ... Thoroughly, the pseudo-code and flowchart of DIRK-REF is shown in Appendix A.4. |
| Open Source Code | Yes | Source code is available at https://github.com/wu-dd/DIRK. |
| Open Datasets | Yes | We evaluated our method on seven commonly used benchmark image dataset: Fashion-MNIST (Xiao, Rasul, and Vollgraf 2017), Kuzushiji-MNIST (Clanuwat et al. 2018), CIFAR-10 (Krizhevsky, Hinton et al. 2009), CIFAR100 (Krizhevsky, Hinton et al. 2009), CUB-200 (Welinder et al. 2010), Flower (Nilsback and Zisserman 2008) and Oxford-IIIT Pet (Parkhi et al. 2012). |
| Dataset Splits | Yes | The hyperparameters were selected so as to maximize the accuracy on a validation set (10% of the training set). |
| Hardware Specification | Yes | Our implementation was executed using Py Torch (Paszke et al. 2019), and all experiments were conducted with NVIDIA Tesla V100 GPU. |
| Software Dependencies | No | Our implementation was executed using Py Torch (Paszke et al. 2019). While PyTorch is mentioned, a specific version number is not provided. |
| Experiment Setup | Yes | For all methods on benchmark datasets, we used SGD as the optimizer with a momentum of 0.9, a weight decay of 1e3, an initial learning rate of 1e-2, and set the epoch number to 500. ... We set the momentum hyperparameter m as 0.99 and the trade-off parameter λ as 0 in DIRK. ... temperature hyperparameters τ1 = 0.01, τ2 = 0.07, and the sizes of both queues are fixed to be 1024. For large-scale datasets CUB-200, Flower, and Oxford-IIIT Pet, we set the mini-batch size as 32, while 256 for other datasets. |