Abstract: A method for determining an encoder architecture of a convolutional neural network configured to process image processing tasks. For each image processing task), characteristic scale distribution is calculated based on training data. Encoder architecture candidates are generated, each including a shared encoder layer providing computational operations for image processing tasks and branches which span over encoder layers providing at least partly different computational operations for the image processing tasks. Each branch is associated with a certain image processing task. Receptive encoder layer field sizes and assessment measures are calculated, each assessment measure referring to a combination of a certain encoder architecture and a certain image processing task, and including information regarding matching quality of characteristic scale distribution associated with the assessment measure to the receptive field sizes of the encoder layers.

Abstract: A method for determining an encoder architecture of a convolutional neural network configured to process image processing tasks. For each image processing task), characteristic scale distribution is calculated based on training data. Encoder architecture candidates are generated, each including a shared encoder layer providing computational operations for image processing tasks and branches which span over encoder layers providing at least partly different computational operations for the image processing tasks. Each branch is associated with a certain image processing task. Receptive encoder layer field sizes and assessment measures are calculated, each assessment measure referring to a combination of a certain encoder architecture and a certain image processing task, and including information regarding matching quality of characteristic scale distribution associated with the assessment measure to the receptive field sizes of the encoder layers.