Recurrent Inference Machines for Inverse  Problems

Berkeley Statistics and Machine Learning Forum

From Compressed Sensing to Deep Learning

Inverse Problems

y = \mathbf{A} x + n




The Bayesian Approach

  • p(y | x) is the likelihood function, contains the physical modeling of the problem:


  • p(x) is the prior, contains our assumptions about the solution
  • Typically, people look for a point estimate of the solution, the Maximum A Posteriori solution:
p(x | y) \propto p(y | x) p(x)
p(y | x) = \mathcal{N}( \mathbf{A} x, \sigma^2)
\hat{x}_{MAP} = \argmax_x \log p(y |x) + \log p(x)

Where does my prior come from ?

\log p(x) = \parallel \mathbf{W} x \parallel_1
\log p(x) = \parallel x \parallel_\Sigma^2
\log p(x) = \parallel \nabla x \parallel_1

Wavelet Sparsity

Total Variation


Classical Image Priors

Image Restauration by Convex Optimization

  • Simple differentiable priors: gradient descent




  • Non differentiable priors: proximal algorithms



x_{t+1} = x_t + \lambda_t ( \nabla_x \log p(y | x_t) + \nabla_x \log p(x_t) )
\tilde{x}_{t+1} = x_t + \lambda_t ( \nabla_x p(y | x_t) )\\ x_{t+1} = \mathrm{prox}_{\lambda_t p(x)} (\tilde{x}_{t+1})

Unrolling Inference

Interpreting optimization algorithms as Deep Neural Networks

Compressive Sensing for MRI

Credit: Lustig et al. 2008

Solving by ADMM

Advantages of the approach

  • Automatically tune optimization parameters for faster convergence
  • Automatically learns optimal representation dictionary
  • Automatically learns optimal proximal operators

This is great, but can we do better?

Learning the Inference

Reinterpreting MAP inference as a RNN

x_{t+1} = x_t + \lambda_t ( \nabla_x \log p(y | x_t) + \nabla_x \log p(x_t) )
x_{t+1} = x_t + g_\phi(\nabla_x p(y | x_t), x_t )

Why not write this iteration as:

  • Update strategy and step size become implicit
  • Prior becomes implicit
\mathcal{L^{(\phi)}} = \sum_{t=1}^T w_t \mathcal{L(x_t^{(\phi)}, x)}

To match the RNN framework, an additional variable s is introduced  to store an optimization memory state.

We trained three models on these tasks: (1) a Recurrent Inference Machine (RIM) as described in 2, (2) a gradient-descent network (GDN) which does not use the current estimate as an input (compare Andrychowicz et al. [15]), and (3) a feed-forward network (FFN) which uses the same inputs as the RIM but where we replaced the GRU unit with a ReLu layer in order to remove hidden state dependence.

Super-resolution example with factor 3

Applications in the Wild 1/2

All models in this paper were trained on acceleration factors that were randomly sampled from the uniform distribution U (1.5, 5.2).  Sub-sampling patterns were then generated using a Gaussian distribution.

Applications in the wild 2/2

Inverting Strong Gravitational Lensing

Recurrent Inferrence Machines

By eiffl

Recurrent Inferrence Machines

Session on RIMs for the Berkeley Statistics and Machine Learning Forum

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