Abstract. Output encoding often leads to superior accuracies in vari- ous machine learning tasks. In this paper we look at a signif- icant task of cell ...
Output Encoding by Compressed Sensing for Cell Detection with Deep Convnet. June 20, 2018. Authors. Track: Papers. Downloads: Download PDF.
Yao Xue, Nilanjan Ray: Output Encoding by Compressed Sensing for Cell Detection with Deep Convnet. AAAI Workshops 2018: 159-166.
Aug 10, 2017 · We employ random projections to encode the output space to a compressed vector of fixed dimension. Then, CNN regresses this compressed vector ...
This paper experimentally demonstrate that proposed CNN + CS framework (referred to as CNNCS) exceeds the accuracy of the state-of-the-art methods on many ...
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Consequently, we employ random projections to encode the output space to a compressed vector of fixed dimension. Then, CNN regresses this compressed vector from ...
Using random projections, CS converts the sparse, output pixel space into dense and compressed vectors. As a regressor, we use deep convolutional neural net ( ...
In the past, output space encoding using compressed sensing (CS) has been used in conjunction with linear and non-linear predictors. To the best of our ...
Ray, “Output Encoding by Compressed Sensing for Cell Detection with Deep Convnet ... Convolutional Neural Network with Compressed Sensing for Cell Detection ...
Mar 25, 2019 · In this paper, we design a new cell detection and localization algorithm that combines deep convolutional neural network (CNN) and compressed ...
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