Wavelet Pooling for Convolutional Neural Networks
Authors: Travis Williams, Robert Li
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on four benchmark classification datasets demonstrate our proposed method outperforms or performs comparatively with methods like max, mean, mixed, and stochastic pooling. |
| Researcher Affiliation | Academia | Travis Williams Department of Electrical Engineering North Carolina A&T State University Greensboro, NC 27410, USA tlwilli3@aggies.ncat.edu Robert Li Department of Electrical Engineering North Carolina A&T State University Greensboro, NC 27410, USA eeli@ncat.edu |
| Pseudocode | Yes | Figure 4: Wavelet Pooling Forward Propagation Algorithm Figure 5: Wavelet Pooling Backpropagation Algorithm |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We test these methods on benchmark image classification datasets such as Mixed National Institute of Standards and Technology (MNIST) (Lecun et al., 1998), Canadian Institute for Advanced Research (CIFAR-10) (Krizhevsky, 2009), Street House View Numbers (SHVN) (Netzer et al., 2011), and Karolinska Directed Emotional Faces (KDEF) (Lundqvist et al., 1998). |
| Dataset Splits | Yes | For the case with dropout, we use the full training set of 73,257 images, a validation set of 30,000 images we extract from the extra training set of 531,131 images, as well as the full testing set of 26,032 images. |
| Hardware Specification | Yes | All experiments are run on a 64-bit operating system, with an Intel Core i7-6800k CPU @ 3.40 GHz processor, with 64.0 GB of RAM. We utilize two Ge Force Titan X Pascal GPUs with 12 GB of video memory for all training. |
| Software Dependencies | Yes | All CNN experiments use Mat Conv Net (Vedaldi & Lenc, 2015). All simulations in MATLAB R2016b. |
| Experiment Setup | Yes | All training uses stochastic gradient descent (Bottou, 2010). For our proposed method, the wavelet basis is the Haar wavelet... All CNN structures except for MNIST use a network loosely based on Zeilers network (Zeiler & Fergus, 2013). We repeat the experiments with Dropout (Srivastava, 2013) and replace Local Response Normalization (Krizhevsky, 2009) with Batch Normalization (Ioffe & Szegedy, 2015) for CIFAR-10 and SHVN (Dropout only)... All pooling methods use a 2x2 window for an even comparison to the proposed method. |