1,587 research outputs found
Accelerating Deep Learning with Shrinkage and Recall
Deep Learning is a very powerful machine learning model. Deep Learning trains
a large number of parameters for multiple layers and is very slow when data is
in large scale and the architecture size is large. Inspired from the shrinking
technique used in accelerating computation of Support Vector Machines (SVM)
algorithm and screening technique used in LASSO, we propose a shrinking Deep
Learning with recall (sDLr) approach to speed up deep learning computation. We
experiment shrinking Deep Learning with recall (sDLr) using Deep Neural Network
(DNN), Deep Belief Network (DBN) and Convolution Neural Network (CNN) on 4 data
sets. Results show that the speedup using shrinking Deep Learning with recall
(sDLr) can reach more than 2.0 while still giving competitive classification
performance.Comment: The 22nd IEEE International Conference on Parallel and Distributed
Systems (ICPADS 2016
Facial Video-based Remote Physiological Measurement via Self-supervised Learning
Facial video-based remote physiological measurement aims to estimate remote
photoplethysmography (rPPG) signals from human face videos and then measure
multiple vital signs (e.g. heart rate, respiration frequency) from rPPG
signals. Recent approaches achieve it by training deep neural networks, which
normally require abundant facial videos and synchronously recorded
photoplethysmography (PPG) signals for supervision. However, the collection of
these annotated corpora is not easy in practice. In this paper, we introduce a
novel frequency-inspired self-supervised framework that learns to estimate rPPG
signals from facial videos without the need of ground truth PPG signals. Given
a video sample, we first augment it into multiple positive/negative samples
which contain similar/dissimilar signal frequencies to the original one.
Specifically, positive samples are generated using spatial augmentation.
Negative samples are generated via a learnable frequency augmentation module,
which performs non-linear signal frequency transformation on the input without
excessively changing its visual appearance. Next, we introduce a local rPPG
expert aggregation module to estimate rPPG signals from augmented samples. It
encodes complementary pulsation information from different face regions and
aggregate them into one rPPG prediction. Finally, we propose a series of
frequency-inspired losses, i.e. frequency contrastive loss, frequency ratio
consistency loss, and cross-video frequency agreement loss, for the
optimization of estimated rPPG signals from multiple augmented video samples
and across temporally neighboring video samples. We conduct rPPG-based heart
rate, heart rate variability and respiration frequency estimation on four
standard benchmarks. The experimental results demonstrate that our method
improves the state of the art by a large margin.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligenc
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