Deep Generative Models for Distribution-Preserving Lossy Compression
Authors: Michael Tschannen, Eirikur Agustsson, Mario Lucic
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 michaelt@nari.ee.ethz.ch Eirikur Agustsson Google AI Perception eirikur@google.com Mario Lucic Google Brain lucic@google.com |
| 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). |