Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: obtaining a pair of deep learning networks. A number of transferrable layers are determined and a homophily value indicating a level of similarity between layers of the same depth from the pair of the deep learning networks is determined. Upon ascertaining that the homophily value shows that the respective weight vectors of a layer of the same depth are to be combined based on a homophily threshold condition, the respective weight vectors for the layer is combined.

Abstract: Methods and systems for visually focused first-person neural network interpretation are disclosed. A method includes: receiving, by a computing device, an image; determining, by the computing device, feature vectors from the image; determining, by the computing device, a first padding value and a first stride value by inputting the feature vectors into a deep neural network; determining, by the computing device, a second padding value and a second stride value by inputting the feature vectors into at least one multiple regression model; determining, by the computing device, padding by averaging the first padding value and the second padding value; determining, by the computing device, stride by averaging the first stride value and the second stride value; and classifying, by the computing device, the image using a convolutional neural network using the padding and the stride.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: obtaining a pair of deep learning networks. A number of transferrable layers are determined and a homophily value indicating a level of similarity between layers of the same depth from the pair of the deep learning networks is determined. Upon ascertaining that the homophily value shows that the respective weight vectors of a layer of the same depth are to be combined based on a homophily threshold condition, the respective weight vectors for the layer is combined.

Abstract: Methods and systems for visually focused first-person neural network interpretation are disclosed. A method includes: receiving, by a computing device, an image; determining, by the computing device, feature vectors from the image; determining, by the computing device, a first padding value and a first stride value by inputting the feature vectors into a deep neural network; determining, by the computing device, a second padding value and a second stride value by inputting the feature vectors into at least one multiple regression model; determining, by the computing device, padding by averaging the first padding value and the second padding value; determining, by the computing device, stride by averaging the first stride value and the second stride value; and classifying, by the computing device, the image using a convolutional neural network using the padding and the stride.