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. 2021 Oct 6;3(6):e210014. doi: 10.1148/ryai.2021210014

Figure 3:

Illustration of the few-shot learning strategy. The model starts with a large-scale dataset in the source domain (Ds) and learns to differentiate between two given inputs. The trained model is then applied to query images. The limited labeled dataset in the target domain (Dt) is used as a comparison set. The model predicts the class label by comparing the query image with the comparison set. The highest similarity class label is associated with the query. FC = fully connected layer.

Illustration of the few-shot learning strategy. The model starts with a large-scale dataset in the source domain (Ds) and learns to differentiate between two given inputs. The trained model is then applied to query images. The limited labeled dataset in the target domain (Dt) is used as a comparison set. The model predicts the class label by comparing the query image with the comparison set. The highest similarity class label is associated with the query. FC = fully connected layer.