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..
Revisiting Neural Networks for Continual Learning: An Architectural Perspective
Authors: Aojun Lu, Tao Feng, Hangjie Yuan, Xiaotian Song, Yanan Sun
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental validation across various CL settings and scenarios demonstrates that improved architectures are parameter-efficient, achieving state-of-the-art performance of CL while being 86%, 61%, and 97% more compact in terms of parameters than the naive CL architecture in Task IL and Class IL. |
| Researcher Affiliation | Academia | Aojun Lu1 , Tao Feng2 , Hangjie Yuan3 , Xiaotian Song1 and Yanan Sun1 1Sichuan University 2Tsinghua University 3Zhejiang University |
| Pseudocode | No | The paper describes methods and strategies in text but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/byyx666/Arch Craft. |
| Open Datasets | Yes | Benchmark. For the CL scenarios mentioned above, i.e., Task IL and Class IL, we assess network performance on CIFAR-100. Benchmark We choose CIFAR-100 and Imagenet-100 to evaluate the Arch Craft-guided architectures. |
| Dataset Splits | No | The paper mentions training and evaluation on a 'test set' but does not explicitly provide details for a separate validation split, such as percentages, counts, or a specific strategy for creating one. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper mentions following 'Py CIL [Zhou et al., 2023]' for training in Class IL, but it does not provide specific version numbers for Py CIL or any other software dependencies. |
| Experiment Setup | Yes | Implementation Details. For Task IL, we train the model by 60 epochs in the first task and 20 epochs in the subsequent tasks. For Class IL, we follow Py CIL [Zhou et al., 2023] to train the model by 200 epochs in the first task and 70 epochs in the subsequent tasks. In Task IL, the network is trained using a vanilla SGD optimizer, while in Class IL, a replay buffer containing 2,000 examples is employed. |