Multi-region constraint enforcement in deep neural networks
AssigneeAutodesk, Inc.
InventorsMohammadmehdi ATAEI, Hyunmin CHEONG, Adrian BUTSCHER
Life of this patent
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Abstract
Techniques for multi-region constraint enforcement in deep neural networks include receiving training data, training a DNN without constraints on the training data to generate a base model, assigning a unique sign pattern for each of a plurality of disjoint convex regions, enforcing the unique sign pattern for each of the plurality of disjoint convex regions by adjusting parameters of the base model to ensure each disjoint convex region in the plurality of disjoint convex regions lies in a unique affine polytope, fine-tuning the base model with updated parameters, and enforcing an affine constraint on each of the plurality of disjoint convex regions of the DNN.
Claims20
A computer-implemented method for enforcing an affine constraint on each of a plurality of disjoint convex regions on a deep neural network (DNN), the method comprising:
- receiving training data;
- training a DNN without constraints on the training data to generate a base model;
- assigning a unique sign pattern for each of a plurality of disjoint convex regions;
- enforcing the unique sign pattern for each of the plurality of disjoint convex regions by adjusting parameters of the base model to ensure each disjoint convex region in the plurality of disjoint convex regions lies in a unique affine polytope;
- fine-tuning the base model with updated parameters;
- and
- enforcing an affine constraint on each of the plurality of disjoint convex regions of the DNN.
One or more non-transitory computer-readable media storing instructions that, when executed by at least one processor, cause the at least one processor to perform the steps of:
- receiving training data;
- training a DNN without constraints on the training data to generate a base model;
- assigning a unique sign pattern for each of a plurality of disjoint convex regions;
- enforcing the unique sign pattern for each of the plurality of disjoint convex regions by adjusting parameters of the base model to ensure each disjoint convex region in the plurality of disjoint convex regions lies in a unique affine polytope;
- fine-tuning the base model with updated parameters;
- and
- enforcing an affine constraint on each of the plurality of disjoint convex regions of the DNN.
A system, comprising:
- one or more memories storing instructions;
- and
- one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform steps comprising:
- receiving training data;
- training a DNN without constraints on the training data to generate a base model;
- assigning a unique sign pattern for each of a plurality of disjoint convex regions;
- enforcing the unique sign pattern for each of the plurality of disjoint convex regions by adjusting parameters of the base model to ensure each disjoint convex region in the plurality of disjoint convex regions lies in a unique affine polytope;
- fine-tuning the base model with updated parameters;
- and
- enforcing an affine constraint on each of the plurality of disjoint convex regions of the DNN.
Dependencies are inferred from the claim text.