Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification

arXiv:2404.11667v1 Announce Type: new
Abstract: 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 …

abstract advanced architectures arxiv classification contrast cs.ai cs.cv cs.lg data deep learning framework image inference networks stat.ml training type video video data