Feature extraction using multi-task learning

A computer system, comprising:

• a processor;
• a computer-readable memory coupled to the processor, the memory comprising instructions that when executed by the processor perform actions of:
• obtaining, by a deep neural network (DNN), training data, the deep neural network comprising at least one hidden layer;
• obtaining, from a specified hidden layer of the at least one hidden layer, features of the training data, the specified hidden layer being connected respectively to a supervised classification network for classification tasks and an autoencoder based reconstruction network for reconstruction tasks;
• and
• training, based on the obtained features, the DNN, the supervised classification network and the reconstruction network as a whole, the training being guided by the classification tasks and the reconstruction tasks.

A computer program product, comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• obtain, by a deep neural network (DNN), training data, the deep neural network comprising at least one hidden layer;
• obtain, from a specified hidden layer of the at least one hidden layer, features of the training data, the specified hidden layer being connected respectively to a supervised classification network for classification tasks and an autoencoder based reconstruction network for reconstruction tasks;
• and
• train, based on the obtained features, the DNN, the supervised classification network and the reconstruction network as a whole, the training being guided by the classification tasks and the reconstruction tasks.