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Found 6 papers, 1 papers with code
Deep Hashing with Hash-Consistent Large Margin Proxy Embeddings
The use of a fixed set of proxies (weights of the CNN classification layer) is proposed to eliminate this ambiguity, and a procedure to design proxy sets that are nearly optimal for both classification and hashing is introduced.
Gradient Boosted Decision Tree Neural Network
In this paper we propose a method to build a neural network that is similar to an ensemble of decision trees.
Large Margin Discriminant Dimensionality Reduction in Prediction Space
We argue that the intermediate mapping, e. g. boosting predictor, is preserving the discriminant aspects of the data and by controlling the dimension of this mapping it is possible to achieve discriminant low dimensional representations for the data.
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
CompACT cascades are shown to seek an optimal trade-off between accuracy and complexity by pushing features of higher complexity to the later cascade stages, where only a few difficult candidate patches remain to be classified.
Ranked #26 on
Pedestrian Detection
on Caltech
Multi-view Face Detection Using Deep Convolutional Neural Networks
In this paper we propose Deep Dense Face Detector (DDFD), a method that does not require pose/landmark annotation and is able to detect faces in a wide range of orientations using a single model based on deep convolutional neural networks.
Multi-Resolution Cascades for Multiclass Object Detection
SBBoost is a boosting algorithm for maximization of this margin.
