Domain separation neural networks

1. A system comprising:

a shared encoder neural network implemented by one or more computers, wherein the shared encoder neural network is configured to:

receive an input image from a target domain; and

process the input image to generate a shared feature representation of features of the input image that are shared between images from the target domain and images from a source domain different from the target domain, wherein the shared encoder neural network has been trained to generate a shared feature representation for the input image from the target domain that, when combined with a private feature representation for the same input image from the target domain generated by a private target encoder neural network that is specific to the target domain, can be used to accurately reconstruct the input image by a shared decoder neural network; and

a classifier neural network implemented by the one or more computers, wherein the classifier neural network is configured to:

receive the shared feature representation; and

process the shared feature representation to generate a network output for the input image that characterizes the input image.

2. The system of claim 1 , wherein images from the target domain have different low level image statistics than images from images from the source domain.

3. The system of claim 1 , wherein the network output is an object classification output.

4. The system of claim 1 , wherein the network output is a pose estimation output.

5. The system of claim 1 , wherein classifier neural network has been trained on labeled images from the source domain.

6. The system of claim 1 , wherein the shared encoder neural network has been trained to generate shared feature representations for input images from the target domain that are similar to shared feature representations for input images from the source domain.

7. The system of claim 6 , wherein the shared encoder neural network has been trained to generate shared feature representations for input images from the target domain that are different from private feature representations for the same input images from the target domain generated by the private target encoder neural network that is specific to the target domain.

8. The system of claim 7 , wherein the shared encoder neural network has been trained to generate shared feature representations for input images from the source domain that are different from private feature representations for the same input images from the source domain generated by a private source encoder neural network that is specific to the source domain.

9. The system of claim 7 , wherein the shared encoder neural network has been trained to generate a shared feature representation for an input image from the source domain that, when combined with a private feature representation for the same input image generated by the private source encoder neural network, can be used to accurately reconstruct the input image by the shared decoder neural network.

10. The system of claim 9 , wherein the shared encoder neural network, the private source encoder neural network, and the private target encoder neural network are convolutional neural networks with a same architecture but different parameter values.

11. The system of claim 1 , wherein the classifier neural network is a fully-connected neural network.

12. A non-transitory computer-readable storage medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to implement:

a shared encoder neural network, wherein the shared encoder neural network is configured to:

receive an input image from a target domain; and

process the input image to generate a shared feature representation of features of the input image that are shared between images from the target domain and images from a source domain different from the target domain, wherein the shared encoder neural network has been trained to generate a shared feature representation for the input image from the target domain that, when combined with a private feature representation for the same input image from the target domain generated by a private target encoder neural network that is specific to the target domain, can be used to accurately reconstruct the input image by a shared decoder neural network; and

a classifier neural network, wherein the classifier neural network is configured to:

receive the shared feature representation; and

process the shared feature representation to generate a network output for the input image that characterizes the input image.

13. The non-transitory computer-readable storage medium of claim 12 , wherein images from the target domain have different low level image statistics than images from images from the source domain.

14. The non-transitory computer-readable storage medium of claim 12 , wherein the network output is an object classification output or a pose estimation output.

15. A method performed by one or more computers, the method comprising:

receiving an input image from a target domain;

processing the input image from the target domain using a shared encoder neural network, wherein the shared encoder neural network is configured to:

receive the input image from the target domain; and

process the input image to generate a shared feature representation of features of the input image that are shared between images from the target domain and images from a source domain different from the target domain, wherein the shared encoder neural network has been trained to generate a shared feature representation for the input image from the target domain that, when combined with a private feature representation for the same input image from the target domain generated by a private target encoder neural network that is specific to the target domain, can be used to accurately reconstruct the input image by a shared decoder neural network; and

processing the input image from the target domain using a classifier neural network, wherein the classifier neural network is configured to:

receive the shared feature representation; and

process the shared feature representation to generate a network output for the input image that characterizes the input image.

16. The method of claim 15 , wherein images from the target domain have different low level image statistics than images from images from the source domain.

17. The method of claim 15 , wherein the network output is an object classification output.

18. The method of claim 15 , wherein the network output is a pose estimation output.

19. The method of claim 15 , wherein classifier neural network has been trained on labeled images from the source domain.

20. The method of claim 15 , wherein the shared encoder neural network has been trained to generate shared feature representations for input images from the target domain that are similar to shared feature representations for input images from the source domain.

21. The method of claim 20 , wherein the shared encoder neural network has been trained to generate shared feature representations for input images from the target domain that are different from private feature representations for the same input images from the target domain generated by the private target encoder neural network that is specific to the target domain.

22. The method of claim 21 , wherein the shared encoder neural network has been trained to generate shared feature representations for input images from the source domain that are different from private feature representations for the same input images from the source domain generated by a private source encoder neural network that is specific to the source domain.

23. The method of claim 21 , wherein the shared encoder neural network has been trained to generate a shared feature representation for an input image from the source domain that, when combined with a private feature representation for the same input image generated by the private source encoder neural network, can be used to accurately reconstruct the input image by the shared decoder neural network.

24. The method of claim 23 , wherein the shared encoder neural network, the private source encoder neural network, and the private target encoder neural network are convolutional neural networks with a same architecture but different parameter values.

25. The method of claim 15 , wherein the classifier neural network is a fully-connected neural network.