Inference apparatus, method, non-transitory computer readable medium and learning apparatus

An inference apparatus comprising a processor configured to:

• generate an intermediate signal by processing an input signal with a convolutional neural network;
• extract one or more intermediate partial signals each serving as part of the intermediate signal from the intermediate signal;
• calculate a statistic of the one or more intermediate partial signals;
• and
• output an inference result relating to the input signal and corresponding to the statistic.

An inference method comprising:

• extracting one or more partial signals each serving as part of an input signal from the input signal;
• generating one or more intermediate partial signals corresponding to the one or more partial signals by processing the partial signals with a convolutional neural network;
• calculating a statistic of the one or more intermediate partial signals;
• and
• outputting an inference result relating to the input signal according to the statistic.

A non-transitory computer readable medium including computer executable instructions, wherein the instructions, when executed by a processor, cause the processor to perform a method comprising:

• extracting one or more partial signals each serving as part of an input signal from the input signal;
• generating one or more intermediate partial signals corresponding to the one or more partial signals by processing the partial signals with a convolutional neural network;
• calculating a statistic of the one or more intermediate partial signals;
• and
• outputting an inference result relating to the input signal according to the statistic.