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..
Learning Shared Knowledge for Deep Lifelong Learning using Deconvolutional Networks
Authors: Seungwon Lee, James Stokes, Eric Eaton
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two computer vision data sets show that the DF-CNN achieves superior performance in challenging lifelong learning settings, resists catastrophic forgetting, and exhibits reverse transfer to improve previously learned tasks from subsequent experience without retraining. |
| Researcher Affiliation | Collaboration | 1University of Pennsylvania, Philadelphia, PA, USA 2Flatiron Institute, New York, NY, USA EMAIL, jstokes@flatironinstitute.org, EMAIL |
| Pseudocode | Yes | Algorithm 1 DF-CNN (λ, kb Size, transform Size) |
| Open Source Code | No | The paper mentions that 'The online appendix is available on the third author s website at http://www.seas.upenn.edu/ eeaton/papers/Lee2019Learning.pdf', which is a PDF document, not source code for the methodology. |
| Open Datasets | Yes | We generated two lifelong learning problems using the CIFAR-100 [Krizhevsky and Hinton, 2009] and Office-Home [Venkateswara et al., 2017] data sets. |
| Dataset Splits | Yes | For CIFAR-100, we created a series of 10 image classification tasks... and split it into training and validation sets in the ratio 5.6:1 (170 training and 30 validation instances per task). ... The Office-Home dataset... we randomly split the data into those with a 60%, 10%, and 30% ratio, respectively. This results in approximately 550 training, 90 validation, and 250 test instances. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | No | Details of training process, architecture of the networks and hyper-parameters used for each data set are described in Appendix B. Since these details are in an appendix, they are not presented in the main text of the paper. |