Other Abstract | Deep learning is the most general method in the fields of computer vision and pattern recognition. This method uses deep neural network as the base model and builds the mapping between data and tasks. Deep learning achieved the state of the art in many tasks of computer vision, such as recognition, detection, tracking, segmentation etc. Besides, it is also widely used in many fields such as natural language processing(NLP), robots etc.
Although deep learning is the most successful method in artificial intelligence, the drawbacks also are obvious. For example, the deep neural networks need a lot of training data. For a special task in industry, an available model generally needs billions of data. It means the large amount of manual data annotation.
This paper introduced three new methods for generating training images. The first one is the image generation based on the MT method; the second one is the image generation based on CycleGAN; the third method is the image generation based on Matting.
The MT method proposes the hypothesis of linearly smooth transition in the space of pixels, and constructs a convex set supported by original training data. Besides, this method generates new images by sampling samples from the convex set and uses these new images to train deep models. Experiments proved that these new images improved the performance of deep models.
For the training problem of CycleGAN, this paper analyzed the reason of unstable gradients in the process of GAN’s training and proposed the gradients penalty of weights method(GPW). This method restricts the discriminator’s gradients to balance the training speed of discriminator and generator. The experimental results showed that GPW method stabilized network training and improved the quality of generated
image.
For relieving the performance degradation of Matting method in similarly-colored regions of image, Patch Alpha Matting method hires SLIC method to divide an image into multiple super pixels and analyzes the affinity relationships between any two super pixels for finding the similarly-colored regions. Additionally, Patch Alpha Matting method uses semantic feature vectors strengthen the discrimination of matting method in similarly-colored regions to realize the divide-and-conquer method naturally. The experimental results showed that the new images generated by Patch Alpha Matting achieved superior synthetic quality and improved the performance of deep models.
Because these methods in this paper can generate new images with diversified semantic objects and backgrounds rather than similar images with original data, these new methods can be seen as the data augmentation methods but totally different with classical data augmentation method. Besides, these methods can generate images in special scene according to the requirements of application. It is meaningful for using deep learning in special scenes.
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