Learning point cloud augmentation policies

A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one more computers to perform operations for:

• receiving training data for training a machine learning model to perform a particular machine learning task, the training data comprising a plurality of training inputs;
• receiving a plurality of data augmentation policy parameters that define different transformation operations for transforming training inputs before the training inputs are used to train the machine learning model;
• maintaining a plurality of candidate machine learning models and, for each of the candidate machine learning models, data specifying:
• (i) respective values of parameters for the candidate machine learning model, (ii) a subset of the transformation operations, (iii) current values of the data augmentation policy parameters that define the subset of the transformation operations, and (iv) a performance measure of the candidate machine learning model on the particular machine learning task;
• maintaining, for each of the different transformation operations, a quality measure of the transformation operation;
• repeatedly performing the following operations at each of multiple time steps:
• for each of the plurality of candidate machine learning models:
• determining an augmented batch of training data by transforming at least some of the training inputs in the training data in accordance with at least the current values of the plurality of data augmentation policy parameters;
• training the candidate machine learning model using the augmented batch of the training data to determine updated values of the parameters for the candidate machine learning model from the maintained values of the parameters for the candidate machine learning model;
• determining an updated performance measure for the candidate machine learning model in accordance with the updated values of the parameters for the candidate machine learning model;
• determining an updated quality measure for each of the subset of transformation operations;
• updating the maintained data to specify (i) new values of the parameters for the candidate machine learning model, (ii) a new subset of the transformation operations, and (iii) a new performance measure, comprising:
• selecting, based on comparing respective performance measures of the candidate machine learning model and another candidate machine learning model, either the values of the parameter of the candidate machine learning model or the values of the parameters of the other candidate machine learning model as new values of parameters for the candidate machine learning model;
• and
• selecting, based on comparing respective performance measures of the candidate machine learning model and the other candidate machine learning model, new data augmentation policy parameters from the plurality of data augmentation policy parameters.

One or more computer storage media storing instructions that when executed by one or more computers cause the one more computers to perform operations for:

• receiving training data for training a machine learning model to perform a particular machine learning task, the training data comprising a plurality of training inputs;
• receiving a plurality of data augmentation policy parameters that define different transformation operations for transforming training inputs before the training inputs are used to train the machine learning model;
• maintaining a plurality of candidate machine learning models and, for each of the candidate machine learning models, data specifying:
• (i) respective values of parameters for the candidate machine learning model, (ii) a subset of the transformation operations, (iii) current values of the data augmentation policy parameters that define the subset of the transformation operations, and (iv) a performance measure of the candidate machine learning model on the particular machine learning task;
• maintaining, for each of the different transformation operations, a quality measure of the transformation operation;
• repeatedly performing the following operations at each of multiple time steps:
• for each of the plurality of candidate machine learning models:
• determining an augmented batch of training data by transforming at least some of the training inputs in the training data in accordance with at least the current values of the plurality of data augmentation policy parameters;
• training the candidate machine learning model using the augmented batch of the training data to determine updated values of the parameters for the candidate machine learning model from the maintained values of the parameters for the candidate machine learning model;
• determining an updated performance measure for the candidate machine learning model in accordance with the updated values of the parameters for the candidate machine learning model;
• determining an updated quality measure for each of the subset of transformation operations;
• updating the maintained data to specify (i) new values of the parameters for the candidate machine learning model, (ii) a new subset of the transformation operations, and (iii) a new performance measure, comprising:
• selecting, based on comparing respective performance measures of the candidate machine learning model and another candidate machine learning model, either the values of the parameter of the candidate machine learning model or the values of the parameters of the other candidate machine learning model as new values of parameters for the candidate machine learning model;
• and
• selecting, based on comparing respective performance measures of the candidate machine learning model and the other candidate machine learning model, new data augmentation policy parameters from the plurality of data augmentation policy parameters.