Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Incorporating long-range consistency in CNN-based texture generation
Authors: Guillaume Berger, Roland Memisevic
ICLR 2017 | Venue PDF | LLM Run Details | Input Tokens: 13,239 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,963 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL, EMAIL |
| 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. |