Emotional facial expression transfer based on temporal restricted Boltzmann machines

2014 ◽  
Author(s):  
Shuojun Liu ◽  
Dong-Yan Huang ◽  
Weisi Lin ◽  
Minghui Dong ◽  
Haizhou Li ◽  
...  
Keyword(s):  
Facial Expression ◽  
Emotional Facial Expression ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Expression Transfer

Emotional facial expression transfer from a single image via generative adversarial nets

2018 ◽  
Vol 29 (3-4) ◽  
pp. e1819 ◽  
Author(s):  
Fengchun Qiao ◽  
Naiming Yao ◽  
Zirui Jiao ◽  
Zhihao Li ◽  
Hui Chen ◽  
...  
Keyword(s):  
Facial Expression ◽  
Single Image ◽  
Emotional Facial Expression ◽  
Expression Transfer

scholarly journals Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Guanglei Xu ◽  
William S. Oates
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Image Reconstruction ◽  
Image Recognition ◽  
Shannon Entropy ◽  
Reconstruction Error ◽  
Likelihood Method ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
D Wave

AbstractRestricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications such as image recognition, drug discovery, and materials design. The Boltzmann probability distribution is used as a model to identify network parameters by optimizing the likelihood of predicting an output given hidden states trained on available data. Training such networks often requires sampling over a large probability space that must be approximated during gradient based optimization. Quantum annealing has been proposed as a means to search this space more efficiently which has been experimentally investigated on D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature or hyperparameter ($$\beta $$ β ) within the Boltzmann distribution which can strongly influence optimization. Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave hardware during neural network training by maximizing the likelihood or minimizing the Shannon entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental validation on an image recognition problem. Neural network image reconstruction errors are evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude lower image reconstruction error using the maximum likelihood over manually optimizing the hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the Shannon entropy for image reconstruction.

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