Deep Dependency Networks and Advanced Inference Schemes for Multi-Label
  Classification
Shivvrat Arya Yu Xiang Vibhav Gogate
We present a unified framework called deep dependency networks (DDNs) that
combines dependency networks and deep learning architectures for multi-label
classification, with a particular emphasis on image and video data. The primary
advantage of dependency networks is their ease of training, in contrast to
other probabilistic graphical models like Markov networks. In particular, when
combined with deep learning architectures, they provide an intuitive,
easy-to-use loss function for multi-label classification. A drawback of DDNs
compared to Markov networks is their lack of advanced inference schemes,
necessitating the use of Gibbs sampling. To address this challenge, we propose
novel inference schemes based on local search and integer linear programming
for computing the most likely assignment to the labels given observations. We
evaluate our novel methods on three video datasets (Charades, TACoS, Wetlab)
and three image datasets (MS-COCO, PASCAL VOC, NUS-WIDE), comparing their
performance with (a) basic neural architectures and (b) neural architectures
combined with Markov networks equipped with advanced inference and learning
techniques. Our results demonstrate the superiority of our new DDN methods over
the two competing approaches.