Training neural network with budding ensemble architecture based on diversity loss

One or more non-transitory computer-readable media storing instructions executable to perform operations for training a neural network, the operations comprising:

• inputting a training dataset into the neural network;
• selecting a layer in a backbone of the neural network, the layer outputting an intermediate tensor based on the training dataset;
• inputting the intermediate tensor into a first head of the neural network, the first head comprising one or more deep learning operations that compute a first detection tensor;
• inputting the intermediate tensor into a second head of the neural network, the second head comprising the one or more deep learning operations that compute a second detection tensor that is different from the first detection tensor;
• determining a loss for the neural network, the loss comprising a diversity loss, the diversity loss indicating a measurement of similarity between the first detection tensor and the second detection tensor;
• and
• training the neural network by adjusting one or more weights in the backbone, first head, or second head based on the loss.

An apparatus, comprising:

• a computer processor for executing computer program instructions;
• and
• a non-transitory computer-readable memory storing computer program instructions executable by the computer processor to perform operations for training a neural network, the operations comprising:
• inputting a training dataset into the neural network,
• selecting a layer in a backbone of the neural network, the layer outputting an intermediate tensor based on the training dataset,
• inputting the intermediate tensor into a first head of the neural network, the first head comprising one or more deep learning operations that compute a first detection tensor,
• inputting the intermediate tensor into a second head of the neural network, the second head comprising the one or more deep learning operations that compute a second detection tensor that is different from the first detection tensor,
• determining a loss for the neural network, the loss comprising a diversity loss, the diversity loss indicating a measurement of similarity between the first detection tensor and the second detection tensor, and
• training the neural network by adjusting one or more weights in the backbone, first head, or second head based on the loss.