Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
GACL: Exemplar-Free Generalized Analytic Continual Learning
Authors: HUIPING ZHUANG, Yizhu Chen, Di Fang, Run He, Kai Tong, Hongxin Wei, Ziqian Zeng, Cen Chen
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 29,018 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,914 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we conduct extensive experiments where, compared with existing GCIL methods, our GACL exhibits a consistently leading performance across various datasets and GCIL settings. Source code is available at https://github.com/CHEN-YIZHU/GACL. |
| Researcher Affiliation | Academia | 1South China University of Technology, China 2Southern University of Science and Technology, China 3Shenzhen Institute, Hunan University, China 4Pazhou Lab, China |
| Pseudocode | Yes | Algorithm 1 The pseudo-code of GACL. |
| Open Source Code | Yes | Source code is available at https://github.com/CHEN-YIZHU/GACL. |
| Open Datasets | Yes | We conduct experiments on three datasets: CIFAR-100 [38], Image Net-R [39], and Tiny-Image Net [40]. |
| Dataset Splits | Yes | As the regularization term γ is not sensitive in a proper range [7], we adopt this value for all datasets for convenience. We relocate its analysis to Appendix E. The size for the buffer layer WB is set to 5000 for both the GACL and ACIL for convenience. |
| Hardware Specification | Yes | We conduct experiments in Py Torch on one Nvidia Geforce RTX 4090 GPU with a batch size of 64 for training and 128 for inference. |
| Software Dependencies | No | The paper mentions "Py Torch" but does not specify a version number or other software dependencies with version numbers. |
| Experiment Setup | Yes | There are two hyperparameters in the GACL, the regularization term γ and the size of the buffer layer. Here, we adopt γ = 100, which is determined by the grid search of {0, 10, 100, 500, 1000, 10000} on CIFAR-100 (by a 90%-10% train-val split). ... The size for the buffer layer WB is set to 5000 for both the GACL and ACIL for convenience. |