Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures
John R. Hershey, Jonathan Le Roux, Felix Weninger

TL;DR
This paper introduces a framework called deep unfolding that transforms model-based inference algorithms into trainable deep neural network architectures, combining domain knowledge with the flexibility of deep learning.
Contribution
It proposes a novel method to unfold inference algorithms into deep networks, enabling the integration of domain knowledge into neural architectures and demonstrating its effectiveness in speech enhancement.
Findings
Deep unfolding can interpret conventional networks as mean-field inference in Markov random fields.
New architectures can be created using belief propagation instead of mean-field inference.
The approach achieves competitive speech enhancement results with fewer parameters.
Abstract
Model-based methods and deep neural networks have both been tremendously successful paradigms in machine learning. In model-based methods, problem domain knowledge can be built into the constraints of the model, typically at the expense of difficulties during inference. In contrast, deterministic deep neural networks are constructed in such a way that inference is straightforward, but their architectures are generic and it is unclear how to incorporate knowledge. This work aims to obtain the advantages of both approaches. To do so, we start with a model-based approach and an associated inference algorithm, and \emph{unfold} the inference iterations as layers in a deep network. Rather than optimizing the original model, we \emph{untie} the model parameters across layers, in order to create a more powerful network. The resulting architecture can be trained discriminatively to perform…
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Taxonomy
TopicsDomain Adaptation and Few-Shot Learning · Speech Recognition and Synthesis · Speech and Audio Processing
