Topics
deep learningOrganisation
Abstract—Deep architectures have been used in transfer learning applications, with the aim of improving the performance of networks designed for a given problem by reusing knowledge from another problem. In this work we addressed the transfer of knowledge between deep networks used as classifiers of digit and shape images, considering cases where only the set of class labels, or only the data distribution, changed from source to target problem. Our main goal was to study how the performance of knowledge transfer between such problems would be affected by varying the number of layers being retrained and the amount of data used in that retraining. Generally, reusing networks trained for a different label set led to better results than reusing...
Nowadays, the transfer learning technique can be successfully applied in the deep learning field thr...
Nowadays, the transfer learning technique can be successfully applied in the deep learning field thr...
Abstract In this work we explore the idea that, in the presence of a small training set of images, i...
Abstract. Transfer Learning is a paradigm in machine learning to solve a target problem by reusing t...
Training a deep neural network from scratch can be very expensive in terms of resources.In addition,...
Training a deep neural network from scratch can be very expensive in terms of resources.In addition,...
Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained so...
Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained so...
Deep learning has dominated the computer vision field since 2012, but a common criticism of deep lea...
Deep learning has dominated the computer vision field since 2012, but a common criticism of deep lea...
Deep learning method, convolutional neural network (CNN) outperforms conventional machine learning m...
Deep learning has dominated the computer vision field since 2012, but a common criticism of deep lea...
Abstract—Transfer learning is a process that allows reusing a learning machine trained on a problem ...
Nowadays, the transfer learning technique can be successfully applied in the deep learning field thr...
Nowadays, the transfer learning technique can be successfully applied in the deep learning field thr...
Nowadays, the transfer learning technique can be successfully applied in the deep learning field thr...
Nowadays, the transfer learning technique can be successfully applied in the deep learning field thr...
Abstract In this work we explore the idea that, in the presence of a small training set of images, i...
Abstract. Transfer Learning is a paradigm in machine learning to solve a target problem by reusing t...
Training a deep neural network from scratch can be very expensive in terms of resources.In addition,...
Training a deep neural network from scratch can be very expensive in terms of resources.In addition,...
Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained so...
Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained so...
Deep learning has dominated the computer vision field since 2012, but a common criticism of deep lea...
Deep learning has dominated the computer vision field since 2012, but a common criticism of deep lea...
Deep learning method, convolutional neural network (CNN) outperforms conventional machine learning m...
Deep learning has dominated the computer vision field since 2012, but a common criticism of deep lea...
Abstract—Transfer learning is a process that allows reusing a learning machine trained on a problem ...
Nowadays, the transfer learning technique can be successfully applied in the deep learning field thr...
Nowadays, the transfer learning technique can be successfully applied in the deep learning field thr...
Nowadays, the transfer learning technique can be successfully applied in the deep learning field thr...
Nowadays, the transfer learning technique can be successfully applied in the deep learning field thr...
Abstract In this work we explore the idea that, in the presence of a small training set of images, i...
Add to ORKG