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..
AFEC: Active Forgetting of Negative Transfer in Continual Learning
Authors: Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, Yi Zhong
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively evaluate AFEC on a variety of continual learning benchmarks, including CIFAR-10 regression tasks, visual classification tasks and Atari reinforcement tasks, where AFEC effectively improves the learning of new tasks and achieves the state-of-the-art performance in a plug-and-play way. |
| Researcher Affiliation | Academia | 1School of Life Sciences, IDG/Mc Govern Institute for Brain Research, Tsinghua University. 2Tsinghua-Peking Center for Life Sciences. 3Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Center, THBI Lab, Tsinghua University. 4AI Center, University College London. 5IIIS, Tsinghua University. 6Yau Mathematical Sciences Center, Tsinghua University. |
| Pseudocode | Yes | We discuss it in Appendix B.4 with a pseudocode. (from main text) and Algorithm 1: AFEC for Continual Learning (from Appendix B.4). |
| Open Source Code | Yes | Our code is included in supplementary materials. |
| Open Datasets | Yes | Dataset: We evaluate continual learning on a variety of benchmark datasets for visual classification, including CIFAR-100, CUB-200-2011 and Image Net-100. CIFAR-100 [13] contains 100-class colored images of the size 32 32... CUB-200-2011 [31] is a large-scale dataset... Image Net-100 [9] is a subset of i ILSVRC-2012 [23]... |
| Dataset Splits | No | The paper mentions training and testing samples for CIFAR-10 ('50,000 training samples and 10,000 testing samples') and CUB-200-2011 ('30 images per class for training while the rest for testing'), but does not explicitly specify validation dataset splits. |
| Hardware Specification | Yes | All the experiments are conducted on NVIDIA GeForce RTX 2080 Ti GPUs. |
| Software Dependencies | No | The paper mentions algorithms and frameworks (e.g., PPO), but does not explicitly list specific software libraries or dependencies with version numbers (e.g., 'PyTorch 1.9', 'CUDA 11.1'). |
| Experiment Setup | Yes | We follow the implementation of [10] to sequentially learn eight randomly selected Atari games. Specifically, we applies a CNN architecture consisting of 3 convolution layers with 2 fully connected layers and identical PPO [25] for all the methods (detailed in Appendix G). (from Section 4.3) and Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Sec. 4 and Appendix C, F and G. (from Ethics Review Checklist, point 3.b). Appendix C and G indeed provide hyperparameter details. |