Incorporating long-range consistency in CNN-based texture generation
Authors: Guillaume Berger, Roland Memisevic
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS |
| Researcher Affiliation | Academia | Guillaume Berger & Roland Memisevic Department of Computer Science and Operations Research University of Montreal guillaume.berger@umontreal.ca, memisevr@iro.umontreal.ca |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation4 uses Lasagne (Dieleman et al., 2015).4available at https://github.com/guillaumebrg/texture generation |
| Open Datasets | No | Most textures used as references in this paper were taken from textures.com and pixabay.com. This does not provide concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | No specific dataset split information (percentages, counts, or predefined splits) for training, validation, or testing was provided. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running experiments. |
| Software Dependencies | No | Our implementation4 uses Lasagne (Dieleman et al., 2015). The paper mentions "Lasagne" but does not provide a specific version number. |
| Experiment Setup | Yes | for image sizes of roughly 384 384 pixels, we recommend the following δ values per layer (which we used in all our following experiments): {2, 4, 8, 16, 32, 64} for pool1, {2, 4, 8, 16, 32} for pool2, {2, 4, 8, 16} for pool3, and {2, 4, 8} for pool4. |