Systems and methods for conditional generative models
Systems and methods for training a conditional generator model are described. Methods receive a sample, and determine a discriminator loss for the received sample. The discriminator loss is based on an ability to determine whether the sample is generated by the conditional generator model or is a ground truth sample. The method determines a secondary loss for the generated sample and updates the conditional generator model based on an aggregate of the discriminator loss and the secondary loss.
1. A method for training a conditional generator model, the method comprising:
receiving a sample;
determining a discriminator loss for the received sample, wherein the discriminator loss is based on an ability to determine whether the sample is generated by the conditional generator model or is a ground truth sample;
determining a plurality of secondary losses for the generated sample using a binary classification model, a multi-class classification model and a regression model, wherein the binary classification model, the multi-class classification model and the regression model are within the conditional generator model;
updating the conditional generator model based on an aggregate of the discriminator loss and the plurality of secondary losses; and
generating images with one or more landmarks, wherein the generated images have higher diversity than the received sample.
2. The method of claim 1 , wherein the steps of receiving, determining a discriminator loss, determining a plurality of secondary losses, and updating the conditional generator model are performed iteratively.
3. The method of claim 2 , wherein determining the secondary loss for a first iteration is performed using a first secondary loss model and determining the secondary loss for a second iteration is performed using a different second secondary loss model.
4. The method of claim 1 , wherein the sample comprises an associated label and wherein the multi-class classification model is trained to classify the sample into one of a plurality of classes.
5. The method of claim 4 , wherein the classification model is a pre-trained model that is not modified during the training of the conditional generator model.
6. The method of claim 1 , wherein the regression model is trained to predict a continuous aspect of the sample.
7. The method of claim 1 , wherein the aggregate is a weighted average of the discriminator loss and the plurality of secondary losses.
8. The method of claim 7 , wherein the steps of receiving, determining a discriminator loss, determining a plurality of secondary losses, and updating the conditional generator model are performed iteratively, wherein a weight used for calculating the weighted average is different between different iterations.
9. The method of claim 1 further comprising:
generating a set of outputs using the trained conditional generator model, wherein the set of outputs comprises a set of samples and a set of associated output labels; and
training a new model using the set of generated outputs that does not include any samples.
10. A system comprising memory and one or more processors configured to:
receive a sample;
determine a discriminator loss for the received sample, wherein the discriminator loss is based on an ability to determine whether the sample is generated by a conditional generator model or is a ground truth sample;
determine a plurality of secondary losses for the sample using a binary classification model, a multi-class classification model and a regression model, wherein the binary classification model, the multi-class classification model and the regression model are within the conditional generator model;
update the conditional generator model based on an aggregate of the discriminator loss and the plurality of secondary losses; and
generating images with one or more landmarks, wherein the generated images have higher diversity than the received sample.
11. The system of claim 10 , wherein the receiving, determining a discriminator loss, determining a plurality of secondary losses, and updating the conditional generator model are performed iteratively.
12. The system of claim 11 , wherein the one or more processors are configured to determine the secondary loss for a first iteration using a first secondary loss model and determining the secondary loss for a second iteration is performed using a different second secondary loss model.
13. The system claim 10 , wherein the sample comprises an associated label and wherein the multi-class classification model is trained to classify the sample into one of a plurality of classes.
14. The system of claim 13 , wherein the classification model is a pre-trained model that is not modified during the training of the conditional generator model.
15. The system of claim 10 , wherein the regression model is trained to predict a continuous aspect of the sample.
16. The system of claim 10 , wherein the aggregate is a weighted average of the discriminator loss and the secondary loss.
17. The system of claim 16 , wherein the receiving, determining a discriminator loss, determining a secondary loss, and updating the conditional generator model are performed iteratively, wherein a weight used for calculating the weighted average is different between different iterations.
18. The system of claim 10 , wherein the one or more processors are further configured to:
generate a set of outputs using the trained conditional generator model, wherein the set of outputs comprises a set of samples and a set of associated output labels; and
train a new model using the set of generated outputs that does not include any samples.