Computer vision systems and methods for end-to-end training of convolutional neural networks using differentiable dual-decomposition techniques
Computer vision systems and methods for end-to end training of neural networks are provided. The system generates a fixed point algorithm for dual-decomposition of a maximum-a-posteriori inference problem and trains the convolutional neural network and a conditional random field with the fixed point algorithm and a plurality of images of a dataset to learn to perform semantic image segmentation. The system can segment an attribute of an image of the dataset by the trained neural network and the conditional random field.
1. A computer vision system for end-to-end training of a neural network, comprising:
a memory; and
a processor in communication with the memory, the processor:
implementing a fixed point algorithm for dual-decomposition of a maximum-a-posteriori inference problem,
training a neural network to perform semantic image segmentation by applying the fixed point algorithm to training input data, and
processing one or more images to segment an attribute of the one or more images using the trained neural network.
2. The computer vision system of claim 1 , wherein the fixed point algorithm is dual-monotone and sub-differentiable.
3. The computer vision system of claim 1 , wherein the processor executes a parallel dynamic programming layer to implement the fixed point algorithm.
4. The computer vision system of claim 1 , wherein the processor:
determines a smoothed-max operator with negative entropy regularization, the smoothed-max operator rendering the fixed point algorithm fully differentiable, and
executes a parallel dynamic programming layer to implement the fully differentiable fixed point algorithm.
5. The computer vision system of claim 4 , wherein the processor:
determines a forward pass of the smoothed-max operator, and
determines a gradient of the forward pass of the smoothed-max operator.
6. The computer vision system of claim 1 , wherein the processor implements the fixed point algorithm by:
defining a graph with vertices denoting a two-dimensional grid to determine the maximum-a-posteriori inference problem on a Markov random field,
transforming the maximum-a-posteriori inference problem to an integer linear programming problem, and
decomposing the graph having vertical and horizontal connections of arbitrary length into sets of horizontal and vertical chain sub-problems.
7. A computer vision method for end-to-end training of a neural network, comprising the steps of:
implementing a fixed point algorithm for dual-decomposition of a maximum-a-posteriori inference problem;
training a neural network to perform semantic image segmentation by applying the fixed point algorithm to training input data; and
processing one or more images using the trained neural network to segment an attribute of the one or more images.
8. The method of claim 7 , wherein the fixed point algorithm is dual-monotone and sub-differentiable.
9. The method of claim 7 , wherein the processor executes a parallel dynamic programming layer to implement the fixed point algorithm.
10. The method of claim 7 , further comprising:
determining a smoothed-max operator with negative entropy regularization, the smoothed-max operator rendering the fixed point algorithm fully differentiable, and
executing a parallel dynamic programming layer to implement the fully differentiable fixed point algorithm.
11. The method of claim 10 , further comprising:
determining a forward pass of the smoothed-max operator, and
determining a gradient of the forward pass of the smoothed-max operator.
12. The method of claim 7 , further comprising:
defining a graph with vertices denoting a two-dimensional grid to determine the maximum-a-posteriori inference problem on a Markov random field,
transforming the maximum-a-posteriori inference problem to an integer linear programming problem, and
decomposing the graph having vertical and horizontal connections of arbitrary length into sets of horizontal and vertical chain sub-problems.
13. A non-transitory computer readable medium having instructions stored thereon for end-to-end training of a neural network which, when executed by a processor, causes the processor to carry out the steps of:
implementing a fixed point algorithm for dual-decomposition of a maximum-a-posteriori inference problem;
training a neural network to perform semantic image segmentation by applying the fixed point algorithm to training input data; and
processing one or more images using the trained neural network to segment an attribute of the one or more images.
14. The non-transitory computer readable medium of claim 13 , wherein the fixed point algorithm is dual-monotone and sub-differentiable.
15. The non-transitory computer readable medium of claim 13 , further comprising the step of executing a parallel dynamic programming layer to implement the fixed point algorithm.
16. The non-transitory computer readable medium of claim 13 , further comprising the steps of:
determining a smoothed-max operator with negative entropy regularization, the smoothed-max operator rendering the fixed point algorithm fully differentiable, and
executing a parallel dynamic programming layer to implement the fully differentiable fixed point algorithm.
17. The non-transitory computer readable medium of claim 16 , further comprising the steps of:
determining a forward pass of the smoothed-max operator, and
determining a gradient of the forward pass of the smoothed-max operator.
18. The non-transitory compute readable medium of claim 13 , further comprising the steps of:
defining a graph with vertices denoting a two-dimensional grid to determine the maximum-a-posteriori inference problem on a Markov random field,
transforming the maximum-a-posteriori inference problem to an integer linear programming problem, and
decomposing the graph having vertical and horizontal connections of arbitrary length into sets of horizontal and vertical chain sub-problems.