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..
Deep Generative Models for Distribution-Preserving Lossy Compression
Authors: Michael Tschannen, Eirikur Agustsson, Mario Lucic
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present an extensive empirical evaluation of the proposed approach on two standard GAN data sets, Celeb A [19] and LSUN bedrooms [20], realizing the first system that effectively solves the DPLC problem. |
| Researcher Affiliation | Collaboration | Michael Tschannen ETH Zürich EMAIL Eirikur Agustsson Google AI Perception EMAIL Mario Lucic Google Brain EMAIL |
| Pseudocode | No | The paper references algorithms from external works (e.g., 'WGAN algorithm [16, Algorithm 1]'), but does not include any pseudocode or algorithm blocks within its own text. |
| Open Source Code | Yes | Code is available at https://github.com/mitscha/dplc. |
| Open Datasets | Yes | We present an extensive empirical evaluation of the proposed approach on two standard GAN data sets, Celeb A [19] and LSUN bedrooms [20], both downscaled to 64 × 64 resolution. |
| Dataset Splits | No | The paper mentions a 'testing set of 10k samples held out form the respective training set', but does not specify a separate validation set or explicit training/validation/test split percentages. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU model, CPU type, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer [33]', 'DCGAN [30]', 'WGAN [16]', 'WAE [17]', and 'WGAN-GP [28]' but does not provide specific version numbers for these or other libraries/frameworks. |
| Experiment Setup | Yes | We set m = 128, n = 2 for Celeb A, and m = 512, n = 4 for the LSUN bedrooms data set. [...] To train G by means of WAE-MMD and WGAN-GP we use the training parameters form [17] and [28], respectively. For Wasserstein++, we set γ in (11) to 2.5 × 10−5 for Celeb A and to 10−4 for LSUN. Further, we use the same training parameters to solve (8) as for WAE-MMD. Thereby, to compensate for the increase in the reconstruction loss with decreasing rate, we adjust the coefficient of the MMD penalty, λMMD (see Appendix C), proportionally as a function of the reconstruction loss of the CAE baseline, i.e., λMMD(R) = const. · MSECAE(R). |