5,965 research outputs found
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks
Deep neural networks (DNNs) have demonstrated success for many supervised
learning tasks, ranging from voice recognition, object detection, to image
classification. However, their increasing complexity might yield poor
generalization error that make them hard to be deployed on edge devices.
Quantization is an effective approach to compress DNNs in order to meet these
constraints. Using a quasiconvex base function in order to construct a binary
quantizer helps training binary neural networks (BNNs) and adding noise to the
input data or using a concrete regularization function helps to improve
generalization error. Here we introduce foothill function, an infinitely
differentiable quasiconvex function. This regularizer is flexible enough to
deform towards and penalties. Foothill can be used as a binary
quantizer, as a regularizer, or as a loss. In particular, we show this
regularizer reduces the accuracy gap between BNNs and their full-precision
counterpart for image classification on ImageNet.Comment: Accepted in 16th International Conference of Image Analysis and
Recognition (ICIAR 2019
SpotNet - Learned iterations for cell detection in image-based immunoassays
Accurate cell detection and counting in the image-based ELISpot and
FluoroSpot immunoassays is a challenging task. Recently proposed methodology
matches human accuracy by leveraging knowledge of the underlying physical
process of these assays and using proximal optimization methods to solve an
inverse problem. Nonetheless, thousands of computationally expensive iterations
are often needed to reach a near-optimal solution. In this paper, we exploit
the structure of the iterations to design a parameterized computation graph,
SpotNet, that learns the patterns embedded within several training images and
their respective cell information. Further, we compare SpotNet to a
convolutional neural network layout customized for cell detection. We show
empirical evidence that, while both designs obtain a detection performance on
synthetic data far beyond that of a human expert, SpotNet is easier to train
and obtains better estimates of particle secretion for each cell.Comment: 5 pages, 4 figures, 2019 IEEE 16th International Symposium on
Biomedical Imaging (ISBI 2019), Venice, Italy, April 8-11, 201
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