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..
Continual Learning with Deep Generative Replay
Authors: Hanul Shin, Jung Kwon Lee, Jaehong Kim, Jiwon Kim
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our methods in several sequential learning settings involving image classification tasks. |
| Researcher Affiliation | Collaboration | Hanul Shin Massachusetts Institute of Technology SK T-Brain EMAIL Jung Kwon Lee , Jaehong Kim , Jiwon Kim SK T-Brain EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | We tested our model on classifying MNIST handwritten digit database [19]. sequentially trained our model on classifying MNIST and Street View House Number (SVHN) dataset [25] |
| Dataset Splits | No | The paper mentions 'test data' and 'training' but does not explicitly describe training/test/validation dataset splits or mention a specific validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types) used for running its experiments. |
| Software Dependencies | No | The paper mentions techniques like WGAN-GP and GANs framework but does not provide specific software dependencies with version numbers (e.g., library names with versions). |
| Experiment Setup | No | The paper describes general training procedures and concepts like 'learning rates' and 'fine-tuning' but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed system-level training settings. |