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..
BooVAE: Boosting Approach for Continual Learning of VAE
Authors: Evgenii Egorov, Anna Kuzina, Evgeny Burnaev
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate the proposed algorithm on commonly used benchmarks (MNIST, Fashion-MNIST, Not MNIST) and Celeb A for disjoint sequential image generation tasks. |
| Researcher Affiliation | Academia | Evgenii Egorov University of Amsterdam EMAIL Anna Kuzina Vrije Universiteit EMAIL Evgeny Burnaev Skoltech, AIRI EMAIL |
| Pseudocode | Yes | Algorithm 1 Boo VAE algorithm |
| Open Source Code | Yes | We provide code at https://github.com/AKuzina/BooVAE. |
| Open Datasets | Yes | We perform experiments on MNIST, not MNIST, fashion MNIST and Celeb A datasets. |
| Dataset Splits | No | The paper mentions using a 'test dataset' and a 'training dataset' for experiments but does not explicitly provide specific percentages or counts for training, validation, and test splits. |
| Hardware Specification | Yes | In Supp.(B.5) we mention that we use 4 NVIDIA V100 GPU for each experiment. |
| Software Dependencies | No | The paper mentions PyTorch in relation to Inception V3 network but does not explicitly list other software dependencies with specific version numbers in the main text. |
| Experiment Setup | Yes | For each task, we add a new classification head (one fully connected layer) and train for 200 epochs with batch size 500. |