This paper studies the neural architecture search (NAS) problem for developing efficient generator networks.
1 code implementation • 3 Nov 2020 • Bochao Wang, Hang Xu, Jiajin Zhang, Chen Chen, Xiaozhi Fang, Yixing Xu, Ning Kang, Lanqing Hong, Chenhan Jiang, Xinyue Cai, Jiawei Li, Fengwei Zhou, Yong Li, Zhicheng Liu, Xinghao Chen, Kai Han, Han Shu, Dehua Song, Yunhe Wang, Wei zhang, Chunjing Xu, Zhenguo Li, Wenzhi Liu, Tong Zhang
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models.
This paper proposes to learn a lightweight video style transfer network via knowledge distillation paradigm.
To promote the capability of student generator, we include a student discriminator to measure the distances between real images, and images generated by student and teacher generators.
Moreover, we transplant the searched network architecture to other datasets which are not involved in the architecture searching procedure.
This paper expands the strength of deep convolutional neural networks (CNNs) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm.
Generative adversarial networks (GANs) have been successfully used for considerable computer vision tasks, especially the image-to-image translation.