Generating object embeddings from images

A computer-implemented method for end-to-end training of an object embedding system, the method comprising:

• iteratively training an object embedding system on a plurality of images, each of the images depicting an object of a particular type, each iteration of the training comprising:
• providing selected images as input to the object embedding system and generating corresponding embeddings, wherein the object embedding system comprises a thumbnailing neural network and an embedding neural network, wherein each neural network comprises a plurality of consecutive layers that are exclusive of each other, and wherein generating an embedding for an object depicted in an image using the object embedding system comprises:
• generating a thumbnail representation of the object depicted in the image as output of the thumbnailing neural network, wherein the thumbnailing neural network processes an input in accordance with values of a set of thumbnailing neural network parameters to:
• determine values of parameters of a spatial transformation that defines a correspondence between pixels of the thumbnail representation and pixels of the image;
• and
• generate as output the thumbnail representation using the spatial transformation and the image;
• generating an embedding by providing the thumbnail representation as input to the embedding neural network that is configured to process the thumbnail representation in accordance with values of a set of embedding neural network parameters to generate an embedding as output;
• determining gradients based on a loss function to reduce a distance between embeddings for same instances of objects, and to increase the distance between embeddings for different instances of objects;
• and
• adjusting the values of the set of spatial transformer neural network parameters and the values of the set of embedding neural network parameters using the gradients.