US 12,664,429 B1 · Deep neural networks with interpretability

Abstract

A feature sub-network trainer improves robustness of interpretability of a deep neural network (DNN) by increasing the likelihood that the DNN will converge to a global minimum of a cost function of the DNN. After determining a plurality of correctly classified examples of a pre-trained DNN, the trainer extracts from the pre-trained DNN a feature sub-network that includes an input layer of the DNN and one or more subsequent sparsely-connected layers of the DNN. The trainer averages output signals from the sub-network to form an average representation of each class identifiable by the DNN. The trainer relabels each correctly classified example with the appropriate average representation, and then trains the feature sub-network with the relabeled examples. In one demonstration, the feature sub-network trainer improved classification accuracy of a seven-layer convolutional neural network, trained with two thousand examples, from 75% to 83% by reusing the training examples.

Claims20

Dependent claims

A neural network classification method, comprising:

determining a plurality of classified examples of a trained neural network;
extracting, from the trained neural network, a feature network having a plurality of network output nodes;
averaging, for each class of a plurality of classes identifiable by the trained neural network, a plurality of output signals from a same output node of the plurality of network output nodes to form one of a plurality of averaged signals associated with each of the plurality of network output nodes, each of the plurality of output signals generated by inputting a different one of the plurality of classified examples of the class into the feature network;
forming a plurality of labeled examples by labeling each classified example, of the plurality of classified examples, based at least in part on the plurality of averaged signals associated with the plurality of network output nodes;
and
training the feature network with the plurality of labeled examples.

3 more elements

A neural network classification system, comprising:

storage to store a trained neural network;
a processor;
and a network training engine, implemented as machine-readable instructions stored in a memory in electronic communication with the processor, that, when executed by the processor, control the system to:
determine a plurality of classified examples of the trained neural network,
extract, from the trained neural network, a feature network having a plurality of network output nodes,
average, for each class of a plurality of classes identifiable by the trained neural network, a plurality of output signals from a same output node of the plurality of network output nodes to form one of a plurality of averaged signals associated with each of the plurality of network output nodes, each of the plurality of output signals generated by inputting a different one of the plurality of classified examples of the class into the feature network,
form a plurality of labeled examples by labeling each classified example, of the plurality of classified examples, based at least in part on one or more of the plurality of averaged signals associated with one or more of the plurality of network output nodes, and
train the feature network with the labeled examples.

5 more elements

Dependencies are inferred from the claim text.

Deep neural networks with interpretability

Life of this patent

Deep neural networks with interpretability

Life of this patent

Abstract

Claims20