24 citations · 79 across the 21 of their papers we have counts for
6 papers · 1 filter
OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning
José Lezama, Qiang Qiu, Pablo Musé +1
Deep neural networks trained using a softmax layer at the top and the cross-entropy loss are ubiquitous tools for image classification. Yet, this does not naturally enforce intra-c…
LDMNet: Low Dimensional Manifold Regularized Neural Networks
Wei Zhu, Qiang Qiu, Jiaji Huang +3
Deep neural networks have proved very successful on archetypal tasks for which large training sets are available, but when the training data are scarce, their performance suffers f…
ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks
Qiang Qiu, Jose Lezama, Alex Bronstein +1
Hash codes are efficient data representations for coping with the ever growing amounts of data. In this paper, we introduce a random forest semantic hashing scheme that embeds tiny…
Soft Proposal Networks for Weakly Supervised Object Localization
Yi Zhu, Yanzhao Zhou, Qixiang Ye +2
Weakly supervised object localization remains challenging, where only image labels instead of bounding boxes are available during training. Object proposal is an effective componen…
Learning to Succeed while Teaching to Fail: Privacy in Closed Machine Learning Systems
Jure Sokolic, Qiang Qiu, Miguel R. D. Rodrigues +1
Security, privacy, and fairness have become critical in the era of data science and machine learning. More and more we see that achieving universally secure, private, and fair syst…
Oriented Response Networks
Yanzhao Zhou, Qixiang Ye, Qiang Qiu +1
Deep Convolution Neural Networks (DCNNs) are capable of learning unprecedentedly effective image representations. However, their ability in handling significant local and global im…