no code implementations • 22 Jun 2017 • Shuchang Zhou, Yuzhi Wang, He Wen, Qinyao He, Yuheng Zou
Overall, our method improves the prediction accuracies of QNNs without introducing extra computation during inference, has negligible impact on training speed, and is applicable to both Convolutional Neural Networks and Recurrent Neural Networks.
2 code implementations • 14 May 2017 • Shuchang Zhou, Taihong Xiao, Yi Yang, Dieqiao Feng, Qinyao He, Weiran He
In this work, we propose a model that can learn object transfiguration from two unpaired sets of images: one set containing images that "have" that kind of object, and the other set being the opposite, with the mild constraint that the objects be located approximately at the same place.
2 code implementations • 30 Nov 2016 • Qinyao He, He Wen, Shuchang Zhou, Yuxin Wu, Cong Yao, Xinyu Zhou, Yuheng Zou
In addition, we propose balanced quantization methods for weights to further reduce performance degradation.