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Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in language and pretrained convolutional features in vision.
Convolutional networks for images, speech, and time series
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A large annotated corpus for learning natural language inference
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Skip-thought vectors
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ImageNet Large Scale Visual Recognition Challenge
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