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..
GAN Memory with No Forgetting
Authors: Yulai Cong, Miaoyun Zhao, Jianqiao Li, Sijia Wang, Lawrence Carin
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the superiority of our method over existing approaches and its effectiveness in alleviating catastrophic forgetting for lifelong classification problems. |
| Researcher Affiliation | Academia | Yulai Cong Miaoyun Zhao Jianqiao Li Sijia Wang Lawrence Carin Department of Electrical and Computer Engineering Duke University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https:// github.com/Miaoyun Zhao/GANmemory_Lifelong Learning. |
| Open Datasets | Yes | To demonstrate the superiority of our GAN memory over existing replay-based methods, we design a challenging lifelong generation problem consisting of 6 perceptually-distant tasks/datasets (see Figure 5): Flowers [57], Cathedrals [99], Cats [97], Brain-tumor images [15], Chest X-rays [35], and Anime faces.10 The GP-GAN [49] trained on the Celeb A [43] (D0) is selected as the base; other well-behaved GAN models may readily be considered. |
| Dataset Splits | No | The paper mentions training data and testing performance but does not specify explicit train/validation/test dataset splits or their sizes, or reference a standard split that includes a validation set for reproducibility. |
| Hardware Specification | Yes | The Titan Xp GPU used was donated by the NVIDIA Corporation. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) that would allow for reproducible setup of the environment. |
| Experiment Setup | No | Detailed experimental settings are given in Appendix A. |