Artificial intelligence server

1. An artificial intelligence server comprising:

a communication interface configured to communicate with an electronic device; and

at least one processor configured to:

generate an artificial intelligence model including a feature extraction layer having a first parameter for extracting a feature vector and a classification layer having a (2-1)th parameter for classifying input data using the feature vector by training a neural network using general-purpose training data and general-purpose labeling data;

obtain a classification layer having a (2-2)th parameter different from the (2-1)th parameter by training the artificial intelligence model in such a manner that classification training data and classification labeling data are provided to the artificial intelligence model;

update the classification layer having the (2-1) th parameter with the classification layer having the (2-2) th parameter, wherein, even after the artificial intelligence model is trained, the first parameter of the feature extraction layer remains as before the training of the artificial intelligence model; and

transmit the updated classification layer having the (2-2) th parameter to the electronic device.

2. The artificial intelligence server according to claim 1 , wherein the at least one processor is configured to:

replace the classification layer having the (2-1) th parameter with a classification layer having an initial parameter; and

obtain the classification layer having the (2-2) th parameter by training the artificial intelligence model in such a manner that the classification training data and the classification labeling data are provided to the artificial intelligence model.

3. The artificial intelligence server according to claim 1 , wherein the at least one processor is configured to:

adjust a parameter of the feature extraction layer and a parameter of the classification layer, so that an error between an estimated value of the neural network and the general-purpose labeling data is reduced, in the process of training the neural network; and

adjust the parameter of the classification layer, so that an error between an estimated value of the artificial intelligence model and the classification labeling data is reduced, in the process of training the artificial intelligence model.

4. The artificial intelligence server according to claim 1 , wherein the trained artificial intelligence model comprises the feature extraction layer having the first parameter and the classification layer having the (2-2) th parameter, and

wherein the at least one processor is configured to:

separate the classification layer having the (2-2) th parameter from the feature extraction layer; and

transmit the separated classification layer to the electronic device.

5. The artificial intelligence server according to claim 1 , further comprising at least one memory configured to store a plurality of classification layers having different parameters,

wherein the at least one processor is configured to transmit, to the electronic device, one or more classification layers among the plurality of classification layers if a classification layer change request is received from the electronic device.

6. The artificial intelligence server according to claim 1 , further comprising at least one memory configured to store data,

wherein the at least one processor is configured to:

obtain a classification layer corresponding to a first category by updating the classification layer by providing classification training data and classification labeling data corresponding to the first category to the artificial intelligence model, and store the classification layer corresponding to the first category in the at least one memory; and

obtain a classification layer corresponding to a second category by updating the classification layer by providing classification training data and classification labeling data corresponding to the second category to the artificial intelligence model, and store the classification layer corresponding to the second category in the at least one memory.

7. The artificial intelligence server according to claim 6 , wherein the at least one processor is configured to:

receive category selection information from the electronic device; and

transmit, to the electronic device, the classification layer corresponding to the first category if the received category selection information corresponds to the first category.

8. The artificial intelligence server according to claim 1 , wherein at least a part of the classification training data is training data belonging to a specific category, which is included in the general-purpose training data.

9. An operating method of an artificial intelligence server, the operating method comprising:

generating an artificial intelligence model including a feature extraction layer having a first parameter and the classification layer having a (2-1)th parameter by training a neural network using general-purpose training data and general-purpose labeling data;

obtaining a classification layer having a (2-2)th parameter different from the (2-1)th parameter by training the artificial intelligence model in such a manner that classification training data and classification labeling data are provided to the artificial intelligence model;

updating the classification layer having the (2-1)th parameter with the classification layer having the (2-2)th parameter, wherein, even after the artificial intelligence model is trained, the first parameter of the feature extraction layer remains as before the training of the artificial intelligence model; and

transmitting the updated classification layer to an electronic device.

10. The operating method according to claim 9 , wherein the obtaining of the classification layer having the (2-2) th parameter comprises:

replacing the classification layer having the (2-1) th parameter with a classification layer having an initial parameter; and

obtaining the classification layer having the (2-2) th parameter by training the artificial intelligence model in such a manner that the classification training data and the classification labeling data are provided to the artificial intelligence model.

11. The operating method according to claim 9 , wherein the generating of the artificial intelligence model comprises adjusting a parameter of the feature extraction layer and a parameter of the classification layer, so that an error between an estimated value of the neural network and the general-purpose labeling data is reduced, in the process of training the neural network, and

wherein the updating of the classification layer comprises adjusting the parameter of the classification layer, so that an error between an estimated value of the artificial intelligence model and the classification labeling data is reduced, in the process of training the artificial intelligence model.

12. The operating method according to claim 9 , wherein the trained artificial intelligence model comprises the feature extraction layer having the first parameter and the classification layer having the (2-2) th parameter, and

wherein the transmitting of the updated classification layer to the electronic device comprises:

separating the classification layer having the (2-2) th parameter from the feature extraction layer; and

transmitting the separated classification layer to the electronic device.

13. The operating method according to claim 9 , wherein the transmitting of the updated classification layer to the electronic device comprises transmitting, to the electronic device, one or more classification layers among a plurality of classification layers having different parameters if a classification layer change request is received from the electronic device.

14. The operating method according to claim 9 , wherein the updating of the classification layer comprises:

obtaining a classification layer corresponding to a first category by updating the classification layer by providing classification training data and classification labeling data corresponding to the first category to the artificial intelligence model, and storing the classification layer corresponding to the first category in the at least one memory; and

obtaining a classification layer corresponding to a second category by updating the classification layer by providing classification training data and classification labeling data corresponding to the second category to the artificial intelligence model, and storing the classification layer corresponding to the second category in the at least one memory.

15. The operating method according to claim 14 , wherein the transmitting of the updated classification layer to the electronic device comprises:

receiving category selection information from the electronic device; and

transmitting, to the electronic device, the classification layer corresponding to the first category if the received category selection information corresponds to the first category.