| abstract |
The invention provides a method for training a convolutional neural network through a small-scale human face data set, which is characterized in that it comprises steps: Step 1: using the original marked human face sample set to train a convolutional neural network VGG face recognition model; Step 2: Construct a deep convolutional generation confrontation network DCGAN model, and use the original labeled face sample set to train a deep convolutional generation confrontation network; Step 3: Generate an unlabeled face sample set through DCGAN; Step 4: Generate a human face with DCGAN Face dataset annotation; Step 5: Use the original labeled face sample set to train the plug-and-play generation network PPGN; Step 6: Generate a labeled face sample set through PPGN; Step 7: Combine the samples generated by DCGAN and PPGN Set and the original labeled sample set to train the convolutional neural network; Step 8: Repeat the training, that is, repeat steps 4, 5, 6, and 7 multiple times; Step 9, use the original labeled face sample set to fine-tune the VGG network. |