Transfer learning is a methodology where weights from a model trained on one task are taken and either used (a) to construct a fixed feature extractor, (b) as weight initialization and/or fine-tuning.
( Image credit: Subodh Malgonde )
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Hyperspectral image(HSI) classification has been improved with convolutional neural network(CNN) in very recent years.
However, the prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways of aggregating multi-site data for network training.
Given a tight computational budget, it is more cost-effective to focus on optimizing the parameter configuration of transfer learning algorithms (3) The research on CPDP is far from mature where it is "not difficult" to find a better alternative by making a combination of existing transfer learning and classification techniques.
This paper contributes to solving problems related to ambiguity in PICO sentence prediction tasks, as well as highlighting how annotations for training named entity recognition systems are used to train a high-performing, but nevertheless flexible architecture for question answering in systematic review automation.
A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal.
To demonstrate this ambiguity, we construct a modality selector (or disambiguator) network, and this model gets substantially lower accuracy on our challenge set, compared to existing datasets, indicating that our questions are more ambiguous.
The resulting model significantly outperforms state-of-the-art models with similar accuracy in terms of mCE and inference throughput.
With transfer learning, we show that a new domain can achieve a 59% accuracy without manual effort.