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 [1].
Lifelong Generative Modelling Using Dynamic Expansion Graph Model
Authors: Fei Ye, Adrian G. Bors8857-8865
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Results Unsupervised Lifelong Learning Benchmark Setting. We define a novel benchmark for the log-likelihood estimation under LLL, explained in Appendix-K from SM1. We consider learning multiple tasks defined within a single domain, such as MNIST (Le Cun et al. 1998) and Fashion (Xiao, Rasul, and Vollgraf 2017). Following from (Burda, Grosse, and Salakhutdinov 2015) we divide MNIST and Fashion into five tasks (Zenke, Poole, and Ganguli 2017), called Split MNIST (S-M) and Split Fashion (S-F). We use the cross-domain setting where we aim to learn a sequence of domains, called COFMI, consisting of Caltech 101 (Fei-Fei, Fergus, and Perona 2007), OMNIGLOT (Lake, Salakhutdinov, and Tenenbaum 2015), Fashion, MNIST, Inverse Fashion (IFashion) where each task is associated with a distinct dataset. All databases images are binarized. |
| Researcher Affiliation | Academia | Fei Ye and Adrian G. Bors Department of Computer Science, University of York, York YO10 5GH, UK EMAIL, EMAIL |
| Pseudocode | No | The paper describes the model and its training procedure in text and mathematical formulas but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Supplementary materials (SM) and source code are available1. 1https://github.com/dtuzi123/Expansion-Graph-Model |
| Open Datasets | Yes | We consider learning multiple tasks defined within a single domain, such as MNIST (Le Cun et al. 1998) and Fashion (Xiao, Rasul, and Vollgraf 2017). ... We use the cross-domain setting where we aim to learn a sequence of domains, called COFMI, consisting of Caltech 101 (Fei-Fei, Fergus, and Perona 2007), OMNIGLOT (Lake, Salakhutdinov, and Tenenbaum 2015), Fashion, MNIST, Inverse Fashion (IFashion)... We train various models under CCCOSCZC lifelong learning setting, where each task is associated with one of the datasets: Celeb A (Liu et al. 2015), CACD (Chen, Chen, and Hsu 2014), 3D-Chair (Aubry et al. 2014), Ommiglot (Lake, Salakhutdinov, and Tenenbaum 2015), Image Net* (Krizhevsky, Sutskever, and Hinton 2012), Car (Yang et al. 2015), Zappos (Yu and Grauman 2017), CUB (Wah et al. 2010). |
| Dataset Splits | No | For a given sequence of tasks {T1, . . . , TN} we consider that each Ti is associated with an unlabeled training set QS i and an unlabeled testing set QT i . The model only sees a sequence of training sets {QS 1 , . . . , QS N} while it is evaluated on {QT 1 , . . . , QT N}. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments (e.g., GPU models, CPU types). |
| Software Dependencies | No | The paper does not provide specific software dependency versions (e.g., Python, PyTorch, or other library versions). |
| Experiment Setup | No | The paper mentions a threshold 'τ = 600' for DEGM's component addition. However, it lacks comprehensive details on the experimental setup such as specific hyperparameters (e.g., learning rate, batch size, epochs, optimizer settings) for training the VAE models. |