Fig. 2
From: Creating interpretable deep learning models to identify species using environmental DNA sequences
How the model makes a prediction using the skip connection. The skip connection is separate from the feature extraction, and contributes to learning intuitive prototypes. The output of the convolution and the stacked raw input are concatenated. This array is then compared to every single prototype. Since a single prototype is only of length 5 and this array is of length 35 due to pooling, the comparison step produces a similarity score (using cosine similarity) at 31 different positions within the array. The maximum of these scores is taken as the overall score for a given prototype. Then, each prototype’s score is fed into a single fully-connected linear layer with no bias, which produces a confidence output for each of the 156 classes. The class with the highest confidence becomes the model’s prediction.
