28 citations · 47 across the 14 of their papers we have counts for
Showing 2018Show all
3 papers · 1 filter
stat.ML2018★ 1 cited
Deformable Part Networks
Ziming Zhang, Rongmei Lin, Alan Sullivan
In this paper we propose novel Deformable Part Networks (DPNs) to learn {\em pose-invariant} representations for 2D object recognition. In contrast to the state-of-the-art pose-awa…
cs.LG2018★ 5 cited
Learning towards Minimum Hyperspherical Energy
Weiyang Liu, Rongmei Lin, Zhen Liu +4
Neural networks are a powerful class of nonlinear functions that can be trained end-to-end on various applications. While the over-parametrization nature in many neural networks re…
cs.CV2018
Decoupled Networks
Weiyang Liu, Zhen Liu, Zhiding Yu +5
Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations. Inspired by the observation tha…