BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
Authors: Lars Maaløe, Marco Fraccaro, Valentin Liévin, Ole Winther
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | BIVA is empirically evaluated by (i) an ablation study analyzing each novel component, (ii) likelihood and semi-supervised classification results on binary images, (iii) likelihood results on natural images, and (iv) an analysis of anomaly detection in complex data distributions. |
| Researcher Affiliation | Collaboration | Lars Maaløe Corti Copenhagen Denmark lm@corti.ai Marco Fraccaro Unumed Copenhagen Denmark mf@unumed.com Valentin Liévin & Ole Winther Technical University of Denmark Copenhagen Denmark {valv,olwi}@dtu.dk |
| Pseudocode | No | No pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | Source code (Tensorflow): https://github.com/larsmaaloee/BIVA. Source code (Py Torch): https://github.com/vlievin/biva-pytorch. |
| Open Datasets | Yes | We evaluate BIVA L = 6 in terms of test log-likelihood on statically binarized MNIST [43], dynamically binarized MNIST [28] and dynamically binarized OMNIGLOT [25]. We trained and evaluated BIVA L = 15 on 32x32 CIFAR-10, 32x32 Image Net [57], and another BIVA L = 20 on 64x64 Celeb A [27]. |
| Dataset Splits | No | The paper does not explicitly state the training/validation/test dataset splits with specific percentages or counts. It mentions 'test set' for evaluation but not the full split methodology for training and validation. |
| Hardware Specification | No | The paper does not specify any hardware details like GPU/CPU models or specific machine configurations used for running experiments. |
| Software Dependencies | No | The paper mentions 'Source code (Tensorflow): https://github.com/larsmaaloee/BIVA. Source code (Py Torch): https://github.com/vlievin/biva-pytorch.' but does not specify version numbers for these software dependencies or any other libraries. |
| Experiment Setup | Yes | We employ a free bits strategy with λ = 2 [23] for all experiments to avoid latent variable collapse during the initial training epochs. Trained models are reported with 1 importance weighted sample, L1, and 1000 importance weighted samples, L1e3 [3]. We evaluate the natural image experiments by bits per dimension (bits/dim), L/(hwc log(2)), where h, w, c denote the height, width, and channels respectively. For a detailed description of the experimental setup see Appendix C and the source code. |