Continual Learning with Scaled Gradient Projection
Authors: Gobinda Saha, Kaushik Roy
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments ranging from continual image classification to reinforcement learning tasks and report better performance with less training overhead than the state-of-the-art approaches. |
| Researcher Affiliation | Academia | Elmore Family School of Electrical and Computer Engineering Purdue University, West Lafayette, Indiana, USA gsaha@purdue.edu, kaushik@purdue.edu |
| Pseudocode | Yes | The pseudocode of the algorithm is given in Algorithm 1 in the Appendix B. |
| Open Source Code | Yes | Code available at https://github.com/sahagobinda/sgp. |
| Open Datasets | Yes | Split CIFAR-100, CIFAR-100 Superclass and Split mini Image Net datasets. ... Krizhevsky 2009 ... Vinyals et al. 2016 |
| Dataset Splits | No | The paper mentions training for a certain number of epochs with early stopping criteria, implying a validation set is used, but does not explicitly provide the split percentages or counts for a validation set. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions algorithms and optimizers like PPO and Adam but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | each task in Split CIFAR-100 and CIFAR-100 Superclass is trained for 200 and 50 epochs respectively with early stopping criteria with batch size of 64, whereas each Split mini Image Net task is trained for 10 epochs with batch size of 10. We use the same threshold values (ϵth) as GPM and use scale coefficient (α) of 1, 3 and 10 for Split mini Image Net, CIFAR-100 Superclass and Split CIFAR-100 datasets respectively. |