Figure 3 - uploaded by F. Boray Tek
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The first layer (conv-1) filters at the end of training with MNIST. (a) Gaussian envelopes, (b) scaled filters, (c) output of a sample that was convolved with each filter.
Source publication
Convolutional neural networks have many hyperparameters such as the filter size, number of filters, and pooling size, which require manual tuning. Though deep stacked structures are able to create multi-scale and hierarchical representations, manually fixed filter sizes limit the scale of representations that can be learned in a single convolutiona...
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Context 1
... observe the change in Σ, we calculated its eigenvalues and eigenvectors. The maximum eigenvalue represents the scale, whereas the tangent between the eigenvectors shows the orientation as illustrated in Figure 2. In Figure 3, we can observe the learned envelope functions scale and orientation effects on filters. Smoothing effect of the envelope function over the input is also observed in some outputs (3(c)). ...
Context 2
... maximum eigenvalue represents the scale, whereas the tangent between the eigenvectors shows the orientation as illustrated in Figure 2. In Figure 3, we can observe the learned envelope functions scale and orientation effects on filters. Smoothing effect of the envelope function over the input is also observed in some outputs (3(c)). Figure 4 shows the training loss and classification error plots. ...
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