Using batches of training items for training a network

A method for training a machine-trained (MT) network that classifies inputs into a plurality of categories, the method comprising:

• propagating a plurality of input training items through the MT network to generate a respective output value for each respective input training item, the plurality of input training items comprising input training items for each of the categories;
• identifying a plurality of triplets from the plurality of input training items, wherein each respective triplet comprises two input training items in a respective first category and one input training item in a respective second category, wherein a plurality of the input training items belong to at least two different triplets;
• calculating a value of a loss function as a summation of respective individual loss functions for each of the respective identified triplets, the respective individual loss function for each respective triplet based on the output values generated for the input training items of the triplet;
• and
• training the MT network using the calculated loss function value.

A non-transitory machine-readable medium storing a program which when executed by at least one processing unit trains a machine-trained (MT) network that classifies inputs into a plurality of categories, the program comprising sets of instructions for:

• propagating a plurality of input training items through the MT network to generate a respective output value for each respective input training item, the plurality of input training items comprising input training items for each of the categories;
• identifying a plurality of triplets from the plurality of input training items, wherein each respective triplet comprises two input training items in a respective first category and one input training item in a respective second category, wherein a plurality of the input training items belong to at least two different triplets;
• calculating a value of a loss function as a summation of respective individual loss functions for each of the respective identified triplets, the respective individual loss function for each respective triplet based on the output values generated for the input training items of the triplet;
• and
• training the MT network using the calculated loss function value.