A recognition apparatus based on a deep neural network, a training apparatus and methods thereof. The deep neural network is obtained by inputting training samples comprising positive samples and negative samples into an input layer of the deep neural network and training. The apparatus includes: a judging unit configured to judge that a sample to be recognized is a suspected abnormal sample when confidences of positive sample classes in a classification result outputted by an output layer of the deep neural network are all less than a predefined threshold value. Hence, reliability of a confidence of a classification result outputted by the deep neural network may be efficiently improved.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A recognition apparatus based on a deep neural network, the deep neural network being obtained by inputting training samples comprising positive samples and negative samples into an input layer of the deep neural network and training, the apparatus comprising: a judger configured to judge that a sample to be recognized is a suspected abnormal sample when confidences of positive sample classes in a classification result outputted by an output layer of the deep neural network are all less than a predefined threshold value. 2. The apparatus according to claim 1, wherein the confidences of positive sample classes refer to similarities between the sample to be recognized and the positive sample classes. 3. The apparatus according to claim 1, wherein the apparatus further comprises: an invalidator configured to set negative sample classes and confidences of the negative sample classes to be invalid when the output layer of the deep neural network outputs the classification result. 4. A training apparatus for a deep neural network, comprising: an inputting unit configured to input training samples comprising positive samples and negative samples into an input layer of the deep neural network; a settor configured to, for the positive samples in the training samples, set real-value tags of first positive sample classes of the positive samples to be 1, and set real-value tags of second positive sample classes to be 0; and for the negative samples in the training samples, set real-value tags of all positive sample classes to be 0; and an outputting unit configured to make an output layer of the deep neural network output similarities between the training samples and the positive sample classes. 5. The apparatus according to claim 4, wherein the apparatus further comprises: an acquiror configured to obtain a network loss according to the similarities between the training samples and the positive sample classes outputted by the output layer of the deep neural network and real values of the training samples obtained according to the real-value tags; an adjustor configured to, for the positive samples in the training samples, adjust the network loss according to a predefined weight; and a backward propagator unit configured to perform backward propagation of the deep neural network according to the adjusted network loss. 6. The apparatus according to claim 5, wherein the adjustor adjusts the network loss according to Formula (1): l ′ = { l , if   s ∈ { negative } l × w , if   s ∈ { positive } ; ( 1 ) where, l′ denotes adjusted network loss, l denotes network loss before being adjusted, w denotes predefined weight, s ∈ {negative} denotes that a current training sample is a negative sample, and s ∈ {positive} denotes that a current training sample is a positive sample. 7. An electronic device, comprising the recognition apparatus as claimed in claim 1 or the training apparatus as claimed in claim 4.