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..
Towards Controlled Data Augmentations for Active Learning
Authors: Jianan Yang, Haobo Wang, Sai Wu, Gang Chen, Junbo Zhao
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive empirical experiments, we bring the performance of active learning methods to a new level: an absolute performance boost of 16.99% on CIFAR-10 and 12.25% on SVHN with 1,000 annotated samples. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Zhejiang University, Hangzhou, China. |
| Pseudocode | Yes | We summarize the pseudo-code of our CAMPAL in Algorithm 1 as in the Appendix B. We given the pseudo code of CAMPAL as shown in algorithm 1. |
| Open Source Code | Yes | Codes are available at https://github.com/jnzju/CAMPAL. |
| Open Datasets | Yes | We conduct experiments on four benchmark datasets: Fashion MNIST, SVHN, CIFAR-10, and CIFAR-100. |
| Dataset Splits | No | The paper describes the active learning cycle, including initial dataset sizes and acquisition cycles, but does not specify explicit training/validation/test dataset splits needed to reproduce the experiment in a traditional supervised learning context. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments, such as GPU models, CPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions using an "SGD optimizer" and "Res Net-18 as the architecture" but does not specify other key software components with version numbers (e.g., Python, PyTorch, TensorFlow versions or specific library versions). |
| Experiment Setup | Yes | We adopt Res Net-18 as the architecture and train the model for 300 epochs with an SGD optimizer of learning rate 0.01, momentum 0.9, and weight decay 5e-4. Specifically, all the experiments run 1048576 training iterations with a batch size 64, the model of Res Net-18, an optimizer of SGD of learning rate 0.03, momentum 0.9 and weight decay 0.0005. Some different parameters across these algorithms are shown in Table 7. |