A method of transfer learning includes receiving second data and generating, via a first network, second labels for the second data. In one configuration, the first network has been previously trained on first labels for first data. Additionally, the second labels are generated for training a second network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of transfer learning, comprising: receiving second data; and generating, via a first network, second labels for the second data, the first network having been previously trained on first labels for first data; the second labels being generated for training a second network. 2. The method of claim 1, further comprising training the second network on the second labels and the second data. 3. The method of claim 2, in which a size of the second network is different from a size of the first network. 4. The method of claim 2, further comprising training the second network on third data and third labels. 5. The method of claim 4, further comprising modifying at least one existing boundary of a class in the second network based at least in part on the third labels and the third data. 6. The method of claim 4, further comprising adding at least one new class in the second network based at least in part on the third labels and the third data. 7. The method of claim 1, in which the first data or the first labels are not available after training the first network. 8. The method of claim 1, in which the second data is substantially similar to the first data. 9. The method of claim 1, further comprising: clustering the second data; comparing the clustered second data to third data; and selecting the second data for labeling based at least in part on a similarity to the third data or a difference from the third data. 10. A method of transfer learning, comprising receiving second labels generated by a first network using second data, the first network having been previously trained on first labels and first data; and training a second network on the second labels and the second data. 11. The method of claim 10, further comprising generating, via the first network, the second labels using the second data. 12. The method of claim 11, further comprising: clustering the second data; comparing the clustered second data to third data; and selecting the second data for labeling based at least in part on a similarity to the third data or a difference from the third data. 13. The method of claim 10, in which a size of the second network is different from a size of the first network. 14. The method of claim 10, further comprising training the second network on third labels and third data. 15. The method of claim 14, further comprising modifying at least one existing boundary of a class in the second network based at least in part on the third labels and the third data. 16. The method of claim 14, further comprising adding at least one new class in the second network based at least in part on the third labels and the third data. 17. The method of claim 10, in which the first data or the first labels are not available after training the first network. 18. The method of claim 10, in which the second data is substantially similar to the first data. 19. An apparatus for transfer learning, comprising: a memory unit; and at least one processor coupled to the memory unit, the at least one processor configured: to receive second data; and to generate, via a first network, second labels for the second data, the first network having been previously trained on first labels for first data; the second labels being generated for training a second network. 20. The apparatus of claim 19, in which the at least one processor is further configured to train the second network on the second labels and the second data. 21. The apparatus of claim 20, in which a size of the second network is different from a size of the first network. 22. The apparatus of claim 20, in which the at least one processor is further configured to train the second network on third data and third labels. 23. The apparatus of claim 22, in which the at least one processor is further configured to modify at least one existing boundary of a class in the second network based at least in part on the third labels and the third data. 24. The apparatus of claim 22, in which the at least one processor is further configured to add at least one new class in the second network based at least in part on the third labels and the third data. 25. The apparatus of claim 19, in which the first data or the first labels are not available after training the first network. 26. The apparatus of claim 19, in which the second data is substantially similar to the first data. 27. The apparatus of claim 19, in which the at least one processor is further configured: to cluster the second data; to compare the clustered second data to third data; and to select the second data for labeling based at least in part on a similarity to the third data or a difference from the third data. 28. An apparatus for transfer learning, comprising: a memory unit; and at least one processor coupled to the memory unit, the at least one processor being configured: to receive second labels generated by a first network using second data, the first network having been previously trained on first labels and first data; and to train a second network on the second labels and the second data. 29. The apparatus of claim 28, in which the at least one processor is further configured to generate, via the first network, the second labels using the second data. 30. The apparatus of claim 29, in which the at least one processor is further configured: to cluster the second data; to compare the clustered second data to third data; and to select the second data for labeling based at least in part on a similarity to the third data or a difference from the third data. 31. The apparatus of claim 28, in which a size of the second network is different from a size of the first network. 32. The apparatus of claim 28, in which the at least one processor is further configured to train the second network on third data and third labels. 33. The apparatus of claim 32, in which the at least one processor is further configured to modify at least one existing boundary of a class in the second network based at least in part on the third data and the third labels. 34. The apparatus of claim 32, in which the at least one processor is further configured to add at least one new class in the second network based at least in part on the third data and the third labels. 35. The apparatus of claim 28, in which the first data or first labels are not available after training the first network. 36. The apparatus of claim 28, in which the second data is substantially similar to the first data. 37. An apparatus for transfer learning, comprising: means for receiving second data; and means for generating, via a first network, second labels for the second data, the first network having been previously trained on first labels for first data; the second labels being generated for training a second network. 38. An apparatus for transfer learning, comprising: means for receiving second labels generated by a first network using second data, the first network having been previously trained on first labels and first data; and means for training a second network on the second labels and the second data. 39. A non-transitory computer-readable medium having program code recorded thereon, the program code comprising: program code to receive second data; and program code to generate, via a first network, second labels for the second data, the first network having been previously trained on first labels for first data; the second labels being generated for training a second network. 40. A non-transitory computer-readable medium having program code recorded thereon, the program code comprising: program code to receive second labels generated by a first network using second data, the first network having been previously trained on first labels and first data; and program code to train a second network on the second labels and the second data.