Formalizing the Generalization-Forgetting Trade-off in Continual Learning
Authors: Krishnan Raghavan, Prasanna Balaprakash
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use the CL benchmark [21] for our experiments and retain the experimental settings (hyperparameters) from [21, 44]. For comparison, we use the split-MNIST, permuted-MNIST, and split-Ci FAR100 data sets while considering three scenarios: incremental domain learning (IDL), incremental task learning (ITL), and incremental class learning (ICL). ... All experiments are conducted in Python 3.4 using the pytorch 1.7.1 library with the NVIDIA-A100 GPU for our simulations. |
| Researcher Affiliation | Academia | R. Krishnan1 and Prasanna Balaprakash1,2 1Mathematics and Computer Science Division 2Leadership Computing Facility Argonne National Laboratory kraghavan,pbalapra@anl.gov |
| Pseudocode | Yes | The pseudo code of the BCL is shown in Algorithm 1. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] Refer to supplementary materials |
| Open Datasets | Yes | We use the CL benchmark [21] for our experiments and retain the experimental settings (hyperparameters) from [21, 44]. For comparison, we use the split-MNIST, permuted-MNIST, and split-Ci FAR100 data sets while considering three scenarios: incremental domain learning (IDL), incremental task learning (ITL), and incremental class learning (ICL). |
| Dataset Splits | No | The main paper body does not explicitly detail the training/validation/test dataset splits. It refers to Appendix C in supplementary materials and [21] for these details in the author checklist. |
| Hardware Specification | Yes | All experiments are conducted in Python 3.4 using the pytorch 1.7.1 library with the NVIDIA-A100 GPU for our simulations. |
| Software Dependencies | Yes | All experiments are conducted in Python 3.4 using the pytorch 1.7.1 library with the NVIDIA-A100 GPU for our simulations. |
| Experiment Setup | No | The paper states that experimental settings (hyperparameters) are retained from [21, 44] and refers to Appendix C in supplementary materials for training details, but does not explicitly provide them within the main text. |