Unsupervised Deep Haar Scattering on Graphs
Authors: Xu Chen, Xiuyuan Cheng, Stephane Mallat
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Numerical ExperimentsThe performance of a Haar scattering classification is tested on scrambled images, whose graph geometry is unknown. Results are provided for MNIST and CIFAR-10 image data bases. Classification experiments are also performed on scrambled signals whose samples are on an irregular grid of a sphere. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering, Princeton University, NJ, USA 2D epartement d Informatique, Ecole Normale Sup erieure, Paris, France |
| Pseudocode | No | The paper describes the computational process through text and equations (e.g., equations 1-5 and Figure 1), but it does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | All computations can be reproduced with a software available at www.di.ens.fr/data/scattering/haar. |
| Open Datasets | Yes | MNIST is a data basis with 6 × 10^4 hand-written digit images of size d = 210, with 5 × 10^4 images for training and 10^4 for testing. CIFAR-10 images are color images of 32 × 32 pixels... with a total of 5 × 10^4 training examples and 10^4 testing examples. |
| Dataset Splits | No | The paper states '5 × 10^4 images for training and 10^4 for testing' for MNIST and CIFAR-10 datasets, but does not explicitly describe a separate validation split or the methodology for such a split. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions that 'All computations can be reproduced with a software available at www.di.ens.fr/data/scattering/haar,' but it does not list specific software dependencies or their version numbers. |
| Experiment Setup | Yes | The scattering scale 2J d is the invariance scale. Scattering coefficients are computed up to the a maximum order m, which is set to 4 in all experiments. Indeed, higher order scattering coefficient have a negligible relative energy, which is below 1%. The unsupervised learning algorithm computes N multiresolution approximations, corresponding to N different scattering transforms... The supervised dimension reduction selects a final set of M orthogonalized scattering coefficients. We set M = 1000 in all numerical experiments. |