Quantum mechanics

Algorithm

Temperature control

Process (computing)

Engineering

Fine-tuning

Net (polyhedron)

Control engineering

Face (sociological concept)

Systems engineering

Operating system

Computation

Set (abstract data type)

Parallel computing

Computer science

Classical mechanics

Filter (signal processing)

Self-tuning

Speedup

Physics

PID controller

Task (project management)

Pattern recognition (psychology)

Mathematics

Sociology

Artificial intelligence

Geometry

Programming language

Acceleration

Convolutional neural network

Computer vision

Social science

IET Image Processing

Multi‐task cascaded convolutional neural network (MTCNN) is a human face detection architecture which uses a cascaded structure with three stages (P‐Net, R‐Net and O‐Net). The authors intend to reduce the computation time of the whole process of the MTCNN. They find that the non‐maximum suppression (NMS) processes after the P‐Net occupy over half of the computation time. Therefore, the authors propose a self‐fine‐tuning method which makes the control of computation time for the NMS process easier. Self‐fine‐tuning is a training trick which uses hard samples generated by P‐Net to retrain P‐Net. After self‐fine‐tuning, the distribution of human face probabilities generated by P‐Net is changed, and the tail of distribution becomes thinner. The control of the number of NMS input boxes can be made easier when the distribution has a thinner tail, and choosing a suitable threshold to filter the face boxes will generate less boxes. So the computation time can be reduced. In order to keep the performance of MTCNN, the authors still propose a landmark data set augmentation, which can enhance the performance of the self‐fine‐tuned MTCNN. From the experiments, it is found that the proposed scheme can significantly reduce the computation time of MTCNN.

Acceleration of multi‐task cascaded convolutional networks