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– The current largest training set of $108\,012$ images was collected in $2013$; this is a more appropriate time to train the network as this collection reflects current technology.
– The network architecture is mostly the same as [@Ip2017].
– The network topology is based on a CNN-LSTM model using $3$ convolutional layers (classifier) and $2$ LSTM layers (regressor). We denote as $3\times2\times3$ the number of cells in the first convolutional layer and $4\times2\times2$ the number of cells in the second layer. The final layer (8 hidden nodes) is used to train a linear SVM. The total number of parameters of our model is $6\times10^6$.
We used the original images (randomly cropped to $224\times224$) of the three classes. For the five noisy versions of each image (adding salt, pepper, blur and plus/minus $5\%$ contrast changes) we built $5$ different noisy image datasets of size $16.2k$ ($400$ cats, $400$ dogs, $400$ flowers, $400$ playing card and $400$ cars). We will call the noisy
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