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We investigated this question using a learning transfer paradigm.
Using a learning transfer design, we found evidence for independent temporal representations: learning a spatiotemporal sequence facilitated learning its temporal and ordinal structure alone; learning a temporal and an ordinal structure facilitated learning of a sequence where the two were coupled.
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The non-transfer method does not use a transfer learning way and is a traditional method.
To reveal the common category-related patterns across modalities we used a transfer learning approach that identified the data features that are relevant to all modalities.
The application of wine quality classification is implemented in Zhang [141] using a multi-task transfer learning approach.
The reward value calculation for transferring message between these nodes is described and which is utilized for deriving a policy path of message transfers using a reinforcement learning approach.
The goal, in this case, is to use transfer learning to transfer knowledge from a label abundant image source domain to enhance a target video annotation task which, in this case, has no labels.
Finally, in order to study the generalization between instead of within modalities, we used a so-called transfer learning approach [40].
Our models are learned using the transfer learning trick by exploiting deep architectures that have been pre-trained on imageNet database, and therefore it requires very little annotation for its training.
To the first problem, we use the transfer learning technology to transfer the weights of a projection matrix trained with strict image prior constraint to one convolution layer.
Use a teaspoon to transfer the jam.
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