...-to-sequence supervised learning task using Transformer-based models. We evaluate several pretraining configurations, including BERT2BERT and BERT2GPT2 and quantify the benefits of transfer learning by comparing them against a baseline Seq2Seq Transformer trained from random initialization. Findings All...

... precision, recall and F1-score were accordingly 61.5, 80.5 and 70%. For transfer learning and preprocessing, Lee et al. (2018a , b) employed a GoogLeNet Inception v3 CNN structure. A training, validation and test dataset all contained 3000 periapical diagnostic images. The models' accuracy...

... situations, radiologists' perception plays a critical role in identifying lung melanoma which is incorrectly detected. Deep learning is a new, capable and influential approach for predicting medical images. In this paper, the authors employed deep transfer learning algorithms for intelligent classification...