Add to ORKGThe success of transfer learning on a target task is highly dependent on the selected source data. Instance transfer methods reuse data from the source tasks to augment the training data for the target task. If poorly chosen, this source data may inhibit learning, resulting in negative transfer. The current most widely used algorithm for instance transfer, TrAdaBoost, performs poorly when given irrelevant source data. We present a novel task-based boosting technique for instance transfer that selectively chooses the source knowledge to transfer to the target task. Our approach performs boosting at both the instance level and the task level, assigning higher weight to those source tasks that show positive transferability to the target task,...
Abstract Transfer learning problems are typically framed as leveragingknowledge learned on a source ...
© 2020, Springer Nature Switzerland AG. We propose a novel adaptive transfer learning framework, lea...
The related problems of transfer learning and multitask learning have attracted significant attentio...
Abstract. Instance-based transfer learning methods utilize labeled ex-amples from one domain to impr...
Transfer learning allows leveraging the knowledge of source domains, available a priori, to help tra...
Traditional machine learning makes a basic assumption: the training and test data should be under th...
Traditional machine learning makes a ba-sic assumption: the training and test data should be under t...
Knowledge transfer from previously learned tasks to a new task is a fundamental com-ponent of human ...
Knowledge transfer from previously learned tasks to a new task is a fundamental com-ponent of human ...
Since the transfer learning can employ knowledge in relative domains to help the learning tasks in c...
In a reinforcement learning setting, the goal of transfer learn-ing is to improve performance on a t...
In a reinforcement learning setting, the goal of transfer learning is to improve performance on a ta...
With transfer learning, one set of tasks is used to bias learning and improve performance on another...
Deep transfer learning recently has acquired significant research interest. It makes use of pre-trai...
Deep transfer learning recently has acquired significant research interest. It makes use of pre-trai...
Abstract Transfer learning problems are typically framed as leveragingknowledge learned on a source ...
© 2020, Springer Nature Switzerland AG. We propose a novel adaptive transfer learning framework, lea...
The related problems of transfer learning and multitask learning have attracted significant attentio...
Abstract. Instance-based transfer learning methods utilize labeled ex-amples from one domain to impr...
Transfer learning allows leveraging the knowledge of source domains, available a priori, to help tra...
Traditional machine learning makes a basic assumption: the training and test data should be under th...
Traditional machine learning makes a ba-sic assumption: the training and test data should be under t...
Knowledge transfer from previously learned tasks to a new task is a fundamental com-ponent of human ...
Knowledge transfer from previously learned tasks to a new task is a fundamental com-ponent of human ...
Since the transfer learning can employ knowledge in relative domains to help the learning tasks in c...
In a reinforcement learning setting, the goal of transfer learn-ing is to improve performance on a t...
In a reinforcement learning setting, the goal of transfer learning is to improve performance on a ta...
With transfer learning, one set of tasks is used to bias learning and improve performance on another...
Deep transfer learning recently has acquired significant research interest. It makes use of pre-trai...
Deep transfer learning recently has acquired significant research interest. It makes use of pre-trai...
Abstract Transfer learning problems are typically framed as leveragingknowledge learned on a source ...
© 2020, Springer Nature Switzerland AG. We propose a novel adaptive transfer learning framework, lea...
The related problems of transfer learning and multitask learning have attracted significant attentio...