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..
Continual Learning by Using Information of Each Class Holistically
Authors: Wenpeng Hu, Qi Qin, Mengyu Wang, Jinwen Ma, Bing Liu7797-7805
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation shows that PCL markedly outperforms the state-of-the-art baselines for one or more classes per task. |
| Researcher Affiliation | Academia | 1 Department of Information Science, School of Mathematical Sciences, Peking University 2 Center for Data Science, AAIS, Peking University 3 Wangxuan Institute of Computer Technology, Peking University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We now evaluate the proposed PCL technique (the code can be found here3) and compare it with both classic and the latest baselines" and Footnote 3 "https://github.com/morning-dews/PCL" |
| Open Datasets | Yes | We use four benchmark image classification datasets and two text classification datasets in our experiments: MNIST (Le Cun, Cortes, and Burges 1998), EMNIST-47 (Cohen et al. 2017), CIFAR10 and CIFAR100 (Krizhevsky and Hinton 2009) for images; 20news and DBPedia for text. |
| Dataset Splits | Yes | We randomly select 10% of the examples from the training set of each dataset as the validation set to tune the hyper-parameters. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using SGD as an optimizer and various baselines' code, but it does not provide specific version numbers for ancillary software components or libraries required for reproduction. |
| Experiment Setup | Yes | For training, we use SGD with moment as the optimizer (learning rate = 0.1). We run each experiment five times. For each run of PCL or a baseline, we execute 500 epochs and use the maximum accuracy as the final result of the run. [...] PCL has 3 parameters that need tuning: λ and n in H-reg (Sec. 3.1) and η for transfer (Sec. 3.2). [...] After tuning, we get the best hyperparameters of λ = 0.5 and n = 12. For η, different data have different values, 0.001 for MNIST and EMNIST-47, 0.005 for CIFAR10 and DBPedia, 0.01 for CIFAR100 and 20news. |