CPC G06F 18/2178 (2023.01) [G06F 18/2155 (2023.01); G06F 18/2431 (2023.01); G06N 20/00 (2019.01)] | 16 Claims |
1. A method for training an image classification model performed by a computing device including one or more processors and a memory for storing one or more programs executed by the one or more processors, the method comprising:
a first training step of training a model body and a first head through supervised learning based on a labeled data set subjected to type 1 labeling;
a second training step of training the model body, the first head, and a second head through multi-task learning based on the labeled data set and an unlabeled data set; and
a third training step of training a plurality of third heads through supervised learning based on the labeled data set subjected to type 2 labeling while freezing the model body,
wherein the model body extracts feature vector for input data, and each of the first head, the second head, and the third head generates a classification result based on the feature vector.
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