97 citations · 291 across the 34 of their papers we have counts for
9 papers · 1 filter
AdderNet: Do We Really Need Multiplications in Deep Learning?
Hanting Chen, Yunhe Wang, Chunjing Xu +4
Compared with cheap addition operation, multiplication operation is of much higher computation complexity. The widely-used convolutions in deep neural networks are exactly cross-co…
DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracing
Shaohui Liu, Yinda Zhang, Songyou Peng +3
We propose a differentiable sphere tracing algorithm to bridge the gap between inverse graphics methods and the recently proposed deep learning based implicit signed distance funct…
CARS: Continuous Evolution for Efficient Neural Architecture Search
Zhaohui Yang, Yunhe Wang, Xinghao Chen +5
Searching techniques in most of existing neural architecture search (NAS) algorithms are mainly dominated by differentiable methods for the efficiency reason. In contrast, we devel…
Bringing Giant Neural Networks Down to Earth with Unlabeled Data
Yehui Tang, Shan You, Chang Xu +2
Compressing giant neural networks has gained much attention for their extensive applications on edge devices such as cellphones. During the compressing process, one of the most imp…
SPLINE-Net: Sparse Photometric Stereo through Lighting Interpolation and Normal Estimation Networks
Qian Zheng, Yiming Jia, Boxin Shi +3
This paper solves the Sparse Photometric stereo through Lighting Interpolation and Normal Estimation using a generative Network (SPLINE-Net). SPLINE-Net contains a lighting interpo…
Data-Free Learning of Student Networks
Hanting Chen, Yunhe Wang, Chang Xu +6
Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones…
Self-calibrating Deep Photometric Stereo Networks
Guanying Chen, Kai Han, Boxin Shi +2
This paper proposes an uncalibrated photometric stereo method for non-Lambertian scenes based on deep learning. Unlike previous approaches that heavily rely on assumptions of speci…
Face Image Reflection Removal
Renjie Wan, Boxin Shi, Haoliang Li +2
Face images captured through the glass are usually contaminated by reflections. The non-transmitted reflections make the reflection removal more challenging than for general scenes…
Deep Shape from Polarization
Yunhao Ba, Alex Ross Gilbert, Franklin Wang +6
This paper makes a first attempt to bring the Shape from Polarization (SfP) problem to the realm of deep learning. The previous state-of-the-art methods for SfP have been purely ph…