Generalized rectifier wavelet covariance models for texture synthesis
Authors: Antoine Brochard, Sixin Zhang, Stéphane Mallat
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | 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 antoine.brochard@ens.fr Sixin Zhang Universit e de Toulouse, INP, IRIT, Toulouse, France sixin.zhang@irit.fr 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). |