Deep Network Classification by Scattering and Homotopy Dictionary Learning
Authors: John Zarka, Louis Thiry, Tomas Angles, Stephane Mallat
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Classification results are analyzed on Image Net. Section 4 analyzes image classification results of sparse scattering networks on Image Net 2012. Image Net 2012 (Russakovsky et al., 2015) is a challenging color image dataset of 1.2 million training images and 50,000 validation images, divided into 1000 classes. Table 1: Top 1 and Top 5 accuracy on Image Net with a same MLP classifier applied to different representations... Figure 4 left shows numerically that the ISTC algorithm for W = D minimizes the Lagrangian L(α) = 1/2 Dα β 2 + λ α 1 over α 0, with an exponential convergence which is faster than ISTA and FISTA. This is tested with a dictionary learned by minimizing the classification loss over Image Net. |
| Researcher Affiliation | Collaboration | John Zarka, Louis Thiry, Tomás Angles Département d informatique de l ENS, ENS, CNRS, PSL University, Paris, France {john.zarka,louis.thiry,tomas.angles}@ens.fr Stéphane Mallat Collège de France, Paris, France Flatiron Institute, New York, USA |
| Pseudocode | No | The paper includes architectural diagrams and mathematical equations but does not present structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code to reproduce experiments is available at https://github.com/j-zarka/Sparse Scat Net |
| Open Datasets | Yes | Image Net 2012 (Russakovsky et al., 2015) is a challenging color image dataset of 1.2 million training images and 50,000 validation images, divided into 1000 classes. |
| Dataset Splits | Yes | Image Net 2012 (Russakovsky et al., 2015) is a challenging color image dataset of 1.2 million training images and 50,000 validation images, divided into 1000 classes. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU or CPU models, memory, or cloud computing instance types. |
| Software Dependencies | No | The paper mentions 'Py Torch implementation' and 'Kymatio' software package, but it does not specify version numbers for these software dependencies, which are necessary for reproducibility. |
| Experiment Setup | Yes | The ISTC network illustrated in Figure 2 has N = 12 layers with Re LU and no batch normalization. The sparse code is first calculated with a 1 1 convolutional dictionary D having 2048 vectors. The optimization learns a relatively large factor λ which yields a large approximation error LSx Dα1 / LSx 0.5, and a very sparse code α1 with about 4% non-zero coefficients. It is done with a stochastic gradient descent during 160 epochs using an initial learning rate of 0.01 with a decay of 0.1 at epochs 60 and 120. |