Classification and localization based on annotation information

A computer-implemented method that employs masks and decoders for improved performance, comprising:

• training, by a device operatively coupled to a processor, a convolutional neural network based on a plurality of masks, the convolutional neural network comprising a set of decoders, wherein a number of decoders in the set of decoders is determined during training of the convolutional neural network;
• generating, by the device, and back propagating, by the device, a loss function based on the plurality of masks to tune the convolutional neural network;
• classifying, by the device, an input image based on the convolutional neural network;
• and
• generating, by the device, a multi-dimensional visualization associated with the classified input image.

A computer program product for improving performance of a machine learning system via employment of masks and decoders, the computer program product comprising a computer readable memory having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• train a convolutional neural network based on a plurality of masks, the convolutional neural network comprising a set of decoders, wherein a number of decoders in the set of decoders is determined during training of the convolutional neural network;
• generate and back propagate a loss function based on the plurality of masks to tune the convolutional neural network;
• classify an input image based on the convolutional neural network;
• and
• generate a multi-dimensional visualization associated with the classified input image.