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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Detecting security vulnerabilities in software before they are exploited has been a challenging problem for decades.
For many (minority) languages, the resources needed to train large models are not available.
In addition, we expand the TensorFlow Lite library to include continual learning capabilities, by integrating a simple replay approach into the head of the current transfer learning model.
We present an end-to-end deep network model that performs meeting diarization from single-channel audio recordings.
To avoid the costly annotation of training data for unseen domains, unsupervised domain adaptation (UDA) attempts to provide efficient knowledge transfer from a labeled source domain to an unlabeled target domain.
With the motion model we generate pseudo-labels for a large unlabeled video collection, which enables us to transfer knowledge by learning to predict these pseudo-labels with an appearance model.
On the one hand, it is conceivable that knowledge from one task could be useful for solving a related problem.
In this paper, we present our vision of so called zero-shot learning for databases which is a new learning approach for database components.
The videos are coupled with clinician assessed TETRAS scores, which are used as ground truth labels to train the DNN.
The benefits of using standard process models for data mining, such as the de facto and the most popular, Cross-Industry-Standard-Process model for Data Mining (CRISP-DM) are reduced cost and time.