Deep neural network learning method and apparatus, and category-independent sub-network learning apparatus

In a deep neural network identifying objects classified to a plurality of categories, a deep neural network learning method of learning, using a computer, a category-dependent sub-network used for a specific category, comprising the steps of:

• the computer storing a category-independent sub-network used commonly for said plurality of categories;
• the computer storing said sub-network used for a specific category;
• the computer initializing said sub-network used for a specific category;
• the computer connecting neurons of an output layer of said category-independent sub-network and neurons of an input layer of said sub-network used for a specific category and thereby forming a deep neural network;
• and
• the computer training said sub-network used for a specific category using training data belonging to said specific category while fixing parameters of said category-independent sub-network.

In a deep neural network identifying objects classified to a plurality of categories, a category-independent sub-network learning apparatus for realizing learning of a category-independent sub-network used commonly for said plurality of categories, said apparatus comprising:

• a storage device for storing first, second and third sub-networks;
• and
• a sub-network training device for training said first, second and third sub-networks with training data belonging to first and second categories among said plurality of categories;
• wherein
• said sub-network training apparatus includes
• a deep neural network training device, training a first deep neural network formed by connecting said second sub-network to an output side of said first sub-network with training data belonging to said first category, and training a second deep neural network formed by connecting said third sub-network to an output side of said first sub-network with training data belonging to said second category, and thereby realizing training of said first and second deep neural networks, and
• a sub-network separating device separating, after completion of training by said deep-neural network training device, said first sub-network from other sub-networks and storing it as said category-independent sub-network in a storage medium.

In a deep neural network identifying objects classified to a plurality of categories, a deep neural network learning apparatus for realizing learning of a category-dependent sub-network used for a specific category, comprising:

• a storage device for storing a category-independent sub-network used commonly for said plurality of categories and said sub-network used for a specific category;
• an initializing device for initializing said sub-network used for a specific category;
• a deep neural network forming device connecting neurons of an output layer of said category-independent sub-network and neurons of an input layer of said sub-network used for a specific category and thereby forming a deep neural network;
• and
• a training device training said sub-network used for a specific category using training data belonging to said specific category while fixing parameters of said category-independent sub-network.