Joint Training of Generic CNN-CRF Models with Stochastic Optimization

By Alexander Kirillov, Dmitrij Schlesinger, Shuai Zheng, Bogdan Savchynskyy, Philip H. S. Torr, Carsten Rother. 2015

21 citations recorded by the source corpus.

We propose a new CNN-CRF end-to-end learning framework, which is based on joint stochastic optimization with respect to both Convolutional Neural Network (CNN) and Conditional Random Field (CRF) parameters. While stochastic gradient descent is a standard technique for CNN training, it was not used for joint models so far. We show that our learning method is (i) general, i.e. it applies to arbitrary CNN and CRF architectures and potential functions; (ii) scalable, i.e. it has a low memory footprint and straightforwardly parallelizes on GPUs; (iii) easy in implementation. Additionally, the unified CNN-CRF optimization approach simplifies a potential hardware implementation. We empirically evaluate our method on the task of semantic labeling of body parts in depth images and show that it compares favorably to competing techniques.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Recent Trends in Deep Learning Based Natural Language Processing (2017, neutral): “In such cases, structured prediction techniques such as conditional random field (CRF) are sometimes employed to better capture dependencies between adjacent class labels and finally generate cohesive label sequence giving maximum score to the whole sentence (Kirillov et al., 2015).”
Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks (2017, methodology): “A common approach to optimizing the log marginals is to use the stochastic maximum likelihood method which is also known as persistent contrastive divergence (PCD) [28, 29, 25].”