Visually focused first-person neural network interpretation

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.