Discrete Deep Feature Extraction: A Theory and New Architectures
Authors: Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Boelcskei
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on handwritten digit classification and facial landmark detection including feature importance evaluation complement the theoretical findings. |
| Researcher Affiliation | Academia | Thomas Wiatowski1 WITHOMAS@NARI.EE.ETHZ.CH Michael Tschannen1 MICHAELT@NARI.EE.ETHZ.CH Aleksandar Stani c1 ASTANIC@STUDENT.ETHZ.CH Philipp Grohs2 PHILIPP.GROHS@UNIVIE.AC.AT Helmut B olcskei1 BOELCSKEI@NARI.EE.ETHZ.CH 1Dept. IT & EE, ETH Zurich, Switzerland 2Dept. Math., University of Vienna, Austria |
| Pseudocode | No | The paper focuses on mathematical theory and experimental results but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at http://www.nari.ee.ethz.ch/ commth/research/ |
| Open Datasets | Yes | We use the MNIST dataset of handwritten digits (Le Cun & Cortes, 1998) which comprises 60,000 training and 10,000 test images of size 28x28. ... We use the Caltech 10,000 Web Faces data base (Angelova et al., 2005). |
| Dataset Splits | Yes | The MNIST dataset of handwritten digits (Le Cun & Cortes, 1998) which comprises 60,000 training and 10,000 test images... The penalty parameter of the SVM and the localization parameter of the RBF kernel are selected via 10-fold cross-validation for each combination of wavelet filter, non-linearity, and pooling operator. ... We select 80% of the images uniformly at random to form a training set and use the remaining images for testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'SVM', 'random forests (RF)', 'algorithme a trous', and 'Viola-Jones face detector', but does not provide specific version numbers for any of these. |
| Experiment Setup | Yes | We set D = 3, and compare different network configurations, each defined by a single module (i.e., we use the same filters, non-linearity, and pooling operator in all layers). Specifically, we consider Haar wavelets and reverse biorthogonal 2.2 (RBIO2.2) wavelets (Mallat, 2009), both with J = 3 scales, the non-linearities described in Section 2.2.1, and the pooling operators described in Section 2.3.1 (with S1 = 1 and S2 = 2). We use a SVM with radial basis function (RBF) kernel for classification. ... In both cases, we fix the number of trees to 30 and select the tree depth using out-of-bag error estimates... For the feature extractor ΦΩ we set D = 4, employ Haar wavelets with J = 3 scales and the modulus non-linearity in every network layer, no pooling in the first layer and average pooling with uniform weights 1/S2 d, Sd = 2, in layers d = 2, 3. |