Paper Info
Title
Modout: Learning Multi-Modal Architectures by Stochastic Regularization
Abstract
Model selection methods based on stochastic regularization have been widely used in deep learning due to their simplicity and effectiveness. The well-known Dropout method treats all units, visible or hidden, in the same way, thus ignoring any a priori information related to grouping or structure. Such structure is present in multi-modal learning applications such as affect analysis and gesture recognition, where subsets of units may correspond to individual modalities. Here we describe Modout, a model selection method based on stochastic regularization, which is particularly useful in the multi-modal setting. Different from other forms of stochastic regularization, it is capable of learning whether or when to fuse two modalities in a layer, which is usually considered to be an architectural hyper-parameter by deep learning researchers and practitioners. Modout is evaluated on two real multi-modal datasets. The results indicate improved performance compared to other forms of stochastic regularization. The result on the Montalbano dataset shows that learning a fusion structure by Modout is on par with a state-of-the-art carefully designed architecture.
Year
DOI
Venue
2017
10.1109/FG.2017.59
2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)
Keywords
Field
DocType
Modout,multimodal architecture learning,stochastic regularization,model selection,deep learning,Dropout method,Montalbano dataset,fusion structure learning
Semi-supervised learning,A priori and a posteriori,Gesture recognition,Model selection,Regularization (mathematics),Artificial intelligence,Deep learning,Mathematics,Machine learning,Modal,Regularization perspectives on support vector machines
Conference
ISSN
ISBN
Citations 
2326-5396
978-1-5090-4024-7
3
PageRank 
References 
Authors
0.38
25
4
Name
Order
Citations
PageRank
Fan Li1404.95
Natalia Neverova226514.44
Christian Wolf3102754.93
Graham W. Taylor41523127.22