Density estimation using Real NVP
Authors: Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate its ability to model natural images on four datasets through sampling, log-likelihood evaluation, and latent variable manipulations. We train our model on four natural image datasets: CIFAR-10 [36], Imagenet [52], Large-scale Scene Understanding (LSUN) [70], Celeb Faces Attributes (Celeb A) [41]. |
| Researcher Affiliation | Collaboration | Laurent Dinh Montreal Institute for Learning Algorithms University of Montreal Montreal, QC H3T1J4 Jascha Sohl-Dickstein Google Brain Samy Bengio Google Brain |
| Pseudocode | No | The paper includes diagrams illustrating computational flow (e.g., Figure 2) but does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the methodology described is publicly available. It only mentions 'Tensorflow' in acknowledgments as a tool used. |
| Open Datasets | Yes | We train our model on four natural image datasets: CIFAR-10 [36], Imagenet [52], Large-scale Scene Understanding (LSUN) [70], Celeb Faces Attributes (Celeb A) [41]. |
| Dataset Splits | Yes | We train our model on four natural image datasets: CIFAR-10 [36], Imagenet [52], Large-scale Scene Understanding (LSUN) [70], Celeb Faces Attributes (Celeb A) [41]. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Tensorflow' in the acknowledgments but does not provide a specific version number for it or any other software dependencies. |
| Experiment Setup | Yes | For datasets of images of size 32 32, we use 4 residual blocks with 32 hidden feature maps for the first coupling layers with checkerboard masking. Only 2 residual blocks are used for images of size 64 64. We use a batch size of 64. For CIFAR-10, we use 8 residual blocks, 64 feature maps, and downscale only once. We optimize with ADAM [33] with default hyperparameters and use an L2 regularization on the weight scale parameters with coefficient 5 10 5. |