Learning Data Augmentation Strategies for Object Detection

A computer-implemented method for augmenting labeled data for training machine-learned models, the method comprising:

• accessing a training dataset that comprises a plurality of training examples, wherein a training example of the plurality of training examples has a corresponding label indicating a bounded portion of the training example;
• sampling an augmentation operation from an augmentation policy comprising a plurality of augmentation operations;
• locally modifying the bounded portion of the training example based on applying the sampled augmentation operation to the bounded portion of the training example to generate an augmented training example;
• and
• outputting the augmented training example for training a machine-learned model with a loss based on the label.

A computing system, comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising:
• accessing a training dataset that comprises a plurality of training examples, wherein a training example of the plurality of training examples has a corresponding label indicating a bounded portion of the training example;
• sampling an augmentation operation from an augmentation policy comprising a plurality of augmentation operations;
• locally modifying the bounded portion of the training example based on applying the sampled augmentation operation to the bounded portion of the training example to generate an augmented training example;
• and
• outputting the augmented training example for training a machine-learned model with a loss based on the label.

One or more non-transitory computer-readable media that collectively store a machine-learned model trained on an augmented training example generated based on:

• accessing a training dataset that comprises a plurality of training examples, wherein a training example of the plurality of training examples has a corresponding label indicating a bounded portion of the training example;
• sampling an augmentation operation from an augmentation policy comprising a plurality of augmentation operations;
• locally modifying the bounded portion of the training example based on applying the sampled augmentation operation to the bounded portion of the training example to generate the augmented training example;
• and
• outputting the augmented training example for training the machine-learned model with a loss based on the label.