Differentiable Jaccard Loss Approximation for Training an Artificial Neural Network

A non-transitory computer readable medium having stored therein instructions executable by a computing system to cause the computing system to perform operations comprising:

• obtaining, by a computing system, a training image;
• obtaining, by the computing system, a ground truth mask corresponding to the training image, wherein the ground truth mask represents a desired segmentation of the training image;
• applying an artificial neural network (ANN) on the training image to generate an output segmentation of the training image, wherein the output segmentation of the training image depends on a plurality of weights of the ANN;
• determining a differentiable Jaccard Loss approximation based on the output segmentation of the training image and the ground truth mask;
• based on the differentiable Jaccard Loss approximation, modifying one or more weights of the ANN;
• and
• providing a representation of the ANN as modified to a mobile computing device.

A system comprising:

• one or more processors;
• and
• a non-transitory computer-readable medium, configured to store instructions, that when executed by the one or more processors, cause the system to perform operations comprising:
• obtaining a training image;
• obtaining a ground truth mask corresponding to the training image, wherein the ground truth mask represents a desired segmentation of the training image;
• applying an artificial neural network (ANN) on the training image to generate an output segmentation of the training image, wherein the output segmentation of the training image depends on a plurality of weights of the ANN;
• determining a differentiable Jaccard Loss approximation based on the output segmentation of the training image and the ground truth mask;
• based on the differentiable Jaccard Loss approximation, modifying one or more weights of the ANN;
• and
• providing a representation of the ANN as modified to a mobile computing device.