Fig. 2: SCFF method for processing with convolutional neural network architecture.
a The original batch of images (top row) is taken from the STL-10 dataset50 and is used to generate positive (middle row) and negative examples (bottom row) for demonstration. b The generated positive and negative examples undergo a series of convolutional (Conv.) and pooling (AvgPool or Maxpool) operations to extract relevant features. The blue axes labeled “Avg.” indicate that the goodness-based loss is computed across the channel dimension and then averaged along the height and width dimensions. Output neurons, extracted from each hidden layer via an external average pooling layer, are flattened and concatenated before being passed to a softmax layer for final classification.
