Graph-based Isometry Invariant Representation Learning
Authors: Renata Khasanova, Pascal Frossard
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments In this section we compare our network to the state-of-the-art transformation-invariant classification algorithms. 5.1. Experimental settings We run experiments with different numbers of layers and parameters. ... 5.2. Performance evaluation Here, we compare TIGra Net to state-of-the art algorithms for transformation-invariant image classification tasks... |
| Researcher Affiliation | Academia | 1Ecole Polytechnique F ed erale de Lausanne (EPFL), Lausanne, Switzerland. Correspondence to: Renata Khasanova <renata.khasanova@epfl.ch>, Pascal Frossard <pascal.frossard@epfl.ch>. |
| Pseudocode | No | The paper describes the architecture and methods in prose and figures (Fig. 2, Fig. 3, Fig. 4) but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the availability of the source code for the described methodology. |
| Open Datasets | Yes | MNIST-012. This is a small subset of the MNIST dataset (Le Cun & Cortes, 2010). ... ETH-80. The dataset (Leibe & Schiele, 2003) contains images of 80 objects that belong to 8 classes. |
| Dataset Splits | Yes | MNIST-012. It includes 500 training, 100 validation and 100 test images... Both of these datasets [MNIST-rot/trans] contain 50k training, 3k validation and 9k test images. ...ETH-80...randomly select 2300, 300 of them as the training, validation sets and we use the rest of them for testing. |
| Hardware Specification | Yes | we gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research. |
| Software Dependencies | No | For each architecture, the network is trained using back-propagation with Adam (Kingma & Ba, 2014) optimization. This only names an optimization algorithm, not a specific software dependency with a version number. No other software versions are mentioned. |
| Experiment Setup | Yes | We run experiments with different numbers of layers and parameters. For each architecture, the network is trained using back-propagation with Adam (Kingma & Ba, 2014) optimization. The exact formulas of the partial derivatives and explanation about the initialization of the network parameters are provided in the supplementary material. ...The details about fully-connected layer parameters are given in the Section 5. ...Table 1. Architectures used for the experiments... SC[Kl, M] is a spectral convolutional layer with Kl filters of degree M, DP[Jl] is a dynamic pooling that retains Jl most important values. S[Kmax] is a statistical layer with Kmax the maximum order of Chebyshev polynomials. |