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..
Generalized rectifier wavelet covariance models for texture synthesis
Authors: Antoine Brochard, Sixin Zhang, Stéphane Mallat
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 4 shows synthesis results of our model, compared with state-of-the-art models. Additionally, the code is made publicly available. |
| Researcher Affiliation | Academia | Antoine Brochard ENS, PSL University, Paris, France EMAIL Sixin Zhang Universit e de Toulouse, INP, IRIT, Toulouse, France EMAIL St ephane Mallat Coll ege de France, Paris, France Flatiron Institute, New York, USA |
| Pseudocode | No | The paper describes algorithmic parameters but does not present them in a structured pseudocode or algorithm block format. |
| Open Source Code | Yes | All calculations can be reproduced by a Python software available at https://github.com/ abrochar/wavelet-texture-synthesis. |
| Open Datasets | Yes | The source for the presented textures are given in Appendix A. Our natural texture examples were obtained from the following three sources: CNS NYU8, Textures.com9, Describable Textures Dataset model10 and the Github page of Berger & Memisevic (2017) 11. |
| Dataset Splits | No | The paper describes a texture synthesis process from a single observed image, not a traditional machine learning training/validation/test split on a large dataset. No specific dataset splits are provided for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Python software', 'Matlab software', 'Lasagne', 'Pytorch', and 'scipy.optimize' functions but does not specify version numbers for any of these software dependencies. |
| Experiment Setup | Yes | For all the ALPHA models, we use Morlet wavelets with a maximal scale J = 5 and number of orientations L = 4. To draw the samples, we follow gradient-based sampling algorithms... we use the LBFGS algorithm (Nocedal, 1980) for the optimization of the objective. ... We use the L-BFGS procedure implemented in Pytorch. It runs for 500 iterations and then it is restarted with an initialization obtained from the previous L-BFGS result. This is repeated 10 times to obtain the synthesis (with an additional histogram matching post-processing). |