Before revision:  The learned image feature extractor is then fine-tuned, in a transfer learning setting , for a supervised classification task .
After revision:  The learned image feature extractor is then fine-tuned, in a transfer learning setting , for a supervised task using a small labeled dataset. The end result is a neural network that automatically interprets imagery without requiring textual reports during inference. This approach can be applied to any task for which text-image pairs are readily available .