226 citations · 257 across the 6 of their papers we have counts for
6 papers
Facial beauty prediction fusing transfer learning and broad learning system
Junying Gan, Xiaoshan Xie, Yikui Zhai +3
Facial beauty prediction (FBP) is an important and challenging problem in the fields of computer vision and machine learning. Not only it is easily prone to overfitting due to the…
Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition
Mengting Chen, Xinggang Wang, Heng Luo +2
Deep networks can learn to accurately recognize objects of a category by training on a large number of annotated images. However, a meta-learning challenge known as a low-shot imag…
Diversity Transfer Network for Few-Shot Learning
Mengting Chen, Yuxin Fang, Xinggang Wang +6
Few-shot learning is a challenging task that aims at training a classifier for unseen classes with only a few training examples. The main difficulty of few-shot learning lies in th…
Natural Language Inference over Interaction Space
Yichen Gong, Heng Luo, Jian Zhang
Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis. We introduce Int…
Deep Tempering
Guillaume Desjardins, Heng Luo, Aaron Courville +1
Restricted Boltzmann Machines (RBMs) are one of the fundamental building blocks of deep learning. Approximate maximum likelihood training of RBMs typically necessitates sampling fr…
Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions
Heng Luo, Pierre Luc Carrier, Aaron Courville +1
We apply the spike-and-slab Restricted Boltzmann Machine (ssRBM) to texture modeling. The ssRBM with tiled-convolution weight sharing (TssRBM) achieves or surpasses the state-of-th…