12 citations · 18 across the 8 of their papers we have counts for
4 papers · 1 filter
Transform-Invariant Convolutional Neural Networks for Image Classification and Search
Xu Shen, Xinmei Tian, Anfeng He +2
Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invaria…
Patch Reordering: a Novel Way to Achieve Rotation and Translation Invariance in Convolutional Neural Networks
Xu Shen, Xinmei Tian, Shaoyan Sun +1
Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance on many visual recognition tasks. However, the combination of convolution and pooling operations…
Continuous Dropout
Xu Shen, Xinmei Tian, Tongliang Liu +2
Dropout has been proven to be an effective algorithm for training robust deep networks because of its ability to prevent overfitting by avoiding the co-adaptation of feature detect…
Quantization Networks
Jiwei Yang, Xu Shen, Jun Xing +5
Although deep neural networks are highly effective, their high computational and memory costs severely challenge their applications on portable devices. As a consequence, low-bit q…