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..
Lightweight Learner for Shared Knowledge Lifelong Learning
Authors: Yunhao Ge, Yuecheng Li, Di Wu, Ao Xu, Adam M. Jones, Amanda Sofie Rios, Iordanis Fostiropoulos, shixian wen, Po-Hsuan Huang, Zachary William Murdock, Gozde Sahin, Shuo Ni, Kiran Lekkala, Sumedh Anand Sontakke, Laurent Itti
TMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On a new, very challenging SKILL-102 dataset with 102 image classification tasks (5,033 classes in total, 2,041,225 training, 243,464 validation, and 243,464 test images), we achieve much higher (and SOTA) accuracy over 8 LL baselines, while also achieving near perfect parallelization. |
| Researcher Affiliation | Collaboration | 1 Thomas Lord Department of Computer Science, University of Southern California 2 Neuroscience Graduate Program, University of Southern California 3 Intel Labs 4 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences 5 Dornsife Department of Psychology, University of Southern California |
| Pseudocode | No | The paper describes the algorithms and methods in detail using natural language and mathematical equations (e.g., equations 1-5), and Figure 3 provides a diagram of the algorithm design and overall pipeline. However, there is no explicit section or block labeled 'Pseudocode' or 'Algorithm' with structured, code-like steps. |
| Open Source Code | Yes | Code and data can be found at https://github.com/gyhandy/Shared-Knowledge-Lifelong-Learning |
| Open Datasets | Yes | On a new, very challenging SKILL-102 dataset with 102 image classification tasks (5,033 classes in total, 2,041,225 training, 243,464 validation, and 243,464 test images), we achieve much higher (and SOTA) accuracy over 8 LL baselines, while also achieving near perfect parallelization. Code and data can be found at https://github.com/gyhandy/Shared-Knowledge-Lifelong-Learning |
| Dataset Splits | Yes | On a new, very challenging SKILL-102 dataset with 102 image classification tasks (5,033 classes in total, 2,041,225 training, 243,464 validation, and 243,464 test images) |
| Hardware Specification | Yes | Agents are implemented in py Torch and run on desktop-grade GPUs (e.g., n Vidia 3090, n Vidia 1080). |
| Software Dependencies | No | The paper states 'Agents are implemented in py Torch' but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Pretrained backbone: We use the xception (Chollet, 2017) pretrained on Image Net (Deng et al., 2009)... We use k = 25 clusters for every task (ablation studies in Appendix)... In our experiments, we use m = 5 images/class for every task... Finally, we trained the concatenated vector with Adam optimizer, 0.001 learning rate, and 100 epochs. |