Concurrent image and corresponding multi-channel auxiliary data generation for a generative model

1. A system, comprising:

a memory that stores computer executable components; and

a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:

a multi-channel generator component that generates:

synthetic multi-channel data associated with a synthetic image,

a first data channel associated with a synthetic image,

a second data channel associated with first segmentation data indicative of a segmentation for the synthetic image, and

a third data channel associated with second segmentation data indicative of a remaining segmentation for the synthetic image; and

a post-processing component that generates first mask data indicative of a first ground truth mask for the first segmentation data, and second mask data indicative of a second ground truth mask for the second segmentation data, wherein the first mask data comprises a binary weight for segmentation in an image associated with the synthetic image.

2. The system of claim 1 , wherein the computer executable components further comprise:

a synthetic image generator component that generates the synthetic image.

3. The system of claim 1 , wherein the computer executable components further comprise:

a segmentation component that generates the first segmentation data associated with the second data channel and the second segmentation data associated with the third data channel.

4. The system of claim 1 , wherein the multi-channel generator component generates the synthetic multi-channel data based on a data distribution.

5. The system of claim 4 , wherein the data distribution is associated with a random vector.

6. The system of claim 1 , wherein the multi-channel generator component generates the synthetic multi-channel data based on at least one latent random variable.

7. The system of claim 1 , wherein the multi-channel generator component employs a deep neural network to generate the synthetic multi-channel data.

8. The system of claim 1 , wherein the first mask data comprises a binary mask that corresponds to an annotation associated with the first segmentation data indicative of the segmentation for the synthetic image.

9. The system of claim 8 , wherein the second mask data comprises a binary mask that corresponds to an annotation associated with the first segmentation data indicative of the remaining segmentation for the synthetic image.

10. The system of claim 1 , wherein the first mask data comprises a filter to mask one or more regions in the synthetic image.

11. The system of claim 1 , wherein the first mask data comprises a group of pixels that define a location for segmentation associated with an image, wherein the image is associated with the synthetic image.

12. A method, comprising:

generating, by a system comprising a processor, synthetic multi-channel data associated with a synthetic image, comprising:

generating, by the system, a first data channel associated with the synthetic image,

generating, by the system, a second data channel associated with first segmentation data indicative of a segmentation for the synthetic image, and

generating, by the system, a third data channel associated with second segmentation data indicative of a remaining segmentation for the synthetic image;

generating, by the system, first mask data indicative of a first ground truth mask for the first segmentation data, wherein the first mask data comprises a binary weight for segmentation in an image associated with the synthetic image; and

generating, by the system, second mask data indicative of a second ground truth mask for the second segmentation data.

13. The method of claim 12 , wherein generating the synthetic multi-channel data comprises generating the synthetic multi-channel data based on a data distribution.

14. The method of claim 13 , wherein the data distribution is associated with a random vector.

15. The method of claim 12 , wherein generating the synthetic multi-channel data comprises generating the synthetic multi-channel data based on at least one latent random variable.

16. The method of claim 12 , wherein generating the synthetic multi-channel data comprises using a deep neural network to generate the synthetic multi-channel data.

17. A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:

generating a first data channel associated with a synthetic image;

generating a second data channel associated with first segmentation data indicative of a segmentation for the synthetic image;

generating a third data channel associated with second segmentation data indicative of a remaining segmentation for the synthetic image;

generating first mask data indicative of a first ground truth mask for the first segmentation data, wherein the first mask data comprises a binary weight for segmentation in an image associated with the synthetic image and

generating second mask data indicative of a second ground truth mask for the second segmentation data.

18. The non-transitory machine-readable medium of claim 17 , wherein the first mask data comprises a filter to mask one or more regions in the synthetic image.

19. The non-transitory machine-readable medium of claim 17 , wherein the first mask data comprises a binary mask that corresponds to an annotation associated with the first segmentation data indicative of the segmentation for the synthetic image.

20. The non-transitory machine-readable medium of claim 19 , wherein the second mask data comprises a binary mask that corresponds to an annotation associated with the first segmentation data indicative of the remaining segmentation for the synthetic image.