DeepFlow: Two Convergent Differentiable Approaches for Pairwise Conditional Random Fields
DeepFlow introduces two methods for integrating Pairwise Conditional Random Fields (CRFs) with deep learning, ensuring they are both convergent and differentiable. It presents Parallel Loopy Belief Propagation (LBP) and Mean-Field Iteration (MFI) as iterative estimation frameworks. These approaches allow probabilistic models to be used effectively in image and signal classification by providing stable gradients for neural network training.