The disclosed embodiments illustrate a domain adaptation method for learning transferable feature representations from a source domain for a target domain. The method includes receiving real-time input data comprising labeled instances of the source domain and unlabeled instances of the target domain from a computing device. The method further includes determining source specific representation corresponding to the source domain and a common representation shared between the source domain and the target domain. Based on a positive contribution from the source specific representation and the common representation, the labeled instances of the source domain are classified. The method further includes training a generalized classifier based on a positive contribution from the common representation. The method further includes automatically performing text classification on the unlabeled instances of the target domain based on the trained generalized classifier. The result of the text classification is rendered at a user interface of the computing device.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A domain adaptation method for learning transferable feature representations, by a data processing server, from a source domain for a target domain, the domain adaptation method comprising: receiving, by one or more processors in the data processing server, real-time input data from a computing device over a communication network, wherein the received input data comprises labeled instances of the source domain and unlabeled instances of the target domain; determining, by a source specific processor in the data processing server, source specific representation corresponding to the source domain, and, by a common processor, a common representation shared between the source domain and the target domain; classifying, by a classification processor in the data processing server, the labeled instances of the source domain based on a positive contribution from the source specific processor that provides the source specific representation and the common processor that provides the common representation; training, by a domain regression processor in the data processing server, a generalized classifier based on a positive contribution from the common processor that provides the common representation; and automatically performing, by the one or more processors, text classification on the unlabeled instances of the target domain based on the trained generalized classifier, wherein result of the text classification automatically performed on the unlabeled instances of the target domain is rendered at a user interface of the computing device over the communication network. 2. The domain adaptation method of claim 1, wherein the input data further comprises labeled instances of one or more source domains in addition to the labeled instances of the source domain, wherein the one or more source domains and the source domain constitute a plurality of source domains. 3. The domain adaptation method of claim 2, further comprising determining, by the source specific processor, source specific representation from each of the one or more source domains, by a global processor, a global representation common between the plurality of source domains and the target domain, and, by the common processor, a source representation common between a source-target pair, wherein the source-target pair includes a source domain from the one or more source domains and the target domain. 4. The domain adaptation method of claim 2, further comprising classifying, by the classification processor, the labeled instances of the one or more source domains based on a positive contribution from a corresponding source specific representation, the global representation, and a corresponding source representation. 5. The domain adaptation method of claim 2, further comprising training, by the domain regression processor, the generalized classifier based on a positive contribution from the global representation and the source representation, wherein the trained generalized classifier is utilized to automatically perform the text classification on the unlabeled instances of the target domain. 6. The domain adaptation method of claim 1, further comprising maximizing, by the common processor, loss and classification ability of the domain regression processor by minimizing error of the classification processor to acquire domain independence. 7. The domain adaptation method of claim 1, further comprising predicting, by the domain regression processor, a probability that an instance is from the source domain or the target domain. 8. The domain adaptation method of claim 7, further comprising determining, by the domain regression processor, a domain divergence between a source sample representation and a target sample representation provided by the common processor based on the prediction. 9. The domain adaptation method of claim 1, further comprising updating, by the domain regression processor, one or more parameters associated with the domain regression processor based on a negative direction of a gradient for the common representation provided by the common processor and in direction of a gradient for the source specific representation provided by the source specific processor. 10. The domain adaptation method of claim 1, further comprising updating, by the classification processor, one or more parameters associated with the classification processor in a negative direction of a gradient for the common representation provided by the common processor and the source specific representation provided by the source specific processor. 11. The domain adaptation method of claim 1, further comprises learning, by the domain regression processor, the common representation and the source specific representation, wherein the common representation provides the positive contribution and the source specific representation provides a penalized contribution to the learning by the domain regression processor. 12. A domain adaptation system for learning transferable feature representations, by a data processing server, from a source domain for a target domain, the system comprising: one or more processors in the data processing server configured to receive real-time input data from a computing device over a communication network, wherein the received input data comprises labeled instances of the source domain and unlabeled instances of the target domain; a source specific processor in the data processing server configured to determine source specific representation corresponding to the source domain; a common processor in the data processing server configured to determine a common representation shared between the source domain and the target domain; a classification processor in the data processing server configured to classify the labeled instances of the source domain based on a positive contribution from the source specific processor that provides the source specific representation and the common processor that provides the common representation for a source-target pair; a domain regression processor in the data processing server configured to train a generalized classifier based on a positive contribution from the common processor that provides the common representation; and the one or more processors in the data processing server configured to automatically perform text classification on the unlabeled instances of the target domain based on the trained generalized classifier, wherein result of the text classification automatically performed on the unlabeled instances of the target domain is rendered at a user interface of the computing device over the communication network. 13. The domain adaptation system of claim 12, wherein the input data further comprises labeled instances of one or more source domains in addition to the labeled instances of the source domain, wherein the one or more source domains and the source domain constitute a plurality of source domains. 14. The domain adaptation system of claim 13, wherein the source specific processor is further configured to determine source specific representation from each of the one or more source domains, a global processor is configured to determine a global representation common between the plurality of source domains and the target domain, and the common processor is further configured to determine a source representation common between a source-target pair, wherein the source-target pair includes a source domain from the one or more source domains and the target domain. 15. The domain adaptation system of claim 13, wherein the classification processor is further configured to classify the labeled instances of the one or more source domains based on a positive contribution from a corresponding source specific representation, the global representation, and a corresponding source representation. 16. The domain adaptation system of claim 13, wherein the domain regression processor is further configured to train the generalized classifier based on a positive contribution from the global representation and the source representation, wherein the trained generalized classifier is utilized to automatically perform the text classification on the unlabeled instances of the target domain. 17. The domain adaptation system of claim 12, wherein the domain regression processor is further configured to: predict a probability that an instance is from the source domain or the target domain; and determine a domain divergence between a source sample representation and a target sample representation provided by the common processor based on the prediction. 18. The domain adaptation system of claim 12, wherein the domain regression processor is further configured to update one or more parameters associated with the domain regression processor based on a negative direction of a gradient for the common representation provided by the common processor and in direction of a gradient for the source specific representation provided by the source specific processor. 19. The domain adaptation system of claim 12, wherein the classification processor is further configured to update one or more parameters associated with the classification processor in a negative direction of a gradient for the common representation provided by the common processor and the source specific representation provided by the source specific processor. 20. A computer program product for use with a computer, the computer program product comprising a non-transitory computer readable medium, wherein the non-transitory computer readable medium stores a computer program code for domain adaptation for learning transferable feature representations from a source domain for a target domain, wherein the computer program code is executable by one or more processors in a data processing server to: receive real-time input data from a computing device over a communication network, wherein the received input data comprises labeled instances of the source domain and unlabeled instances of the target domain; determine source specific representation corresponding to the source domain, and, by a common processor, a common representation shared between the source domain and the target domain; classify the labeled instances of the source domain based on a positive contribution from the source specific processor that provides the source specific representation and the common processor that provides the common representation; train a generalized classifier based on a positive contribution from the common processor that provides the common representation; and automatically perform text classification on the unlabeled instances of the target domain based on the trained generalized classifier, wherein result of the text classification automatically performed on the unlabeled instances of the target domain is rendered at a user interface of the computing device over the communication network.