Low level visual feature extraction by learning of multiple tasks for Convolutional Neural Networks
Hidenori Ide, Takio Kurita · 2016
Visual features trained from large scale image data by the deep convolutional neural network can be used for the other visual tasks. This paper investigates the effects of the learning of multiple tasks for such transfer learning from the source domains to the target domain. Two methods of the learning of multiple tasks are considered. Also we investigate which hidden layers should be re-trained for the target task in the fine-tuning process by selecting a subset of the hidden layers and updating only the parameters of the selected subset. Through a detail experiments, we confirmed the effectiveness of the learning of multiple tasks for pre-training. Also we showed that the first layer is not always required to be trained for the target task and the fully-connected layer, the classifier layer, and the last hidden layer should be retrained for the target task in the fine-tuning. These results suggests that the first and the second layers in the deep convolutional neural network trained by the learning of multiple tasks can extract general low level visual features.