794 citations · 2.4k across the 126 of their papers we have counts for
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An Embarrassingly Simple Approach for Knowledge Distillation
Mengya Gao, Yujun Shen, Quanquan Li +5
Knowledge Distillation (KD) aims at improving the performance of a low-capacity student model by inheriting knowledge from a high-capacity teacher model. Previous KD methods typica…
Instance-level Facial Attributes Transfer with Geometry-Aware Flow
Weidong Yin, Ziwei Liu, Chen Change Loy
We address the problem of instance-level facial attribute transfer without paired training data, e.g. faithfully transferring the exact mustache from a source face to a target face…
Deep Network Interpolation for Continuous Imagery Effect Transition
Xintao Wang, Ke Yu, Chao Dong +2
Deep convolutional neural network has demonstrated its capability of learning a deterministic mapping for the desired imagery effect. However, the large variety of user flavors mot…
Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks
Yuenan Hou, Zheng Ma, Chunxiao Liu +1
The training of many existing end-to-end steering angle prediction models heavily relies on steering angles as the supervisory signal. Without learning from much richer contexts, t…
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Xintao Wang, Ke Yu, Shixiang Wu +6
The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the ha…
Improving On-policy Learning with Statistical Reward Accumulation
Yubin Deng, Ke Yu, Dahua Lin +2
Deep reinforcement learning has obtained significant breakthroughs in recent years. Most methods in deep-RL achieve good results via the maximization of the reward signal provided…