Deep Multi-Instance Transfer Learning

Abstract

We present a new approach for transferring knowledge from groups to individuals that comprise them. We evaluate our method in text, by inferring the ratings of individual sentences using full-review ratings. This approach, which combines ideas from transfer learning, deep learning and multi-instance learning, reduces the need for laborious human labelling of fine-grained data when abundant labels are available at the group level.

References in corpus (6)

• Efficient Estimation of Word Representations in Vector Space
• Distributed Representations of Words and Phrases and their Compositionality
• Natural Language Processing (almost) from Scratch
• Distributed Representations of Sentences and Documents
• Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network
• Training Restricted Boltzmann Machines on Word Observations

Cited by in corpus (3)

• Multiple Instance Learning: A Survey of Problem Characteristics and Applications
• Word Embeddings and Their Use In Sentence Classification Tasks
• One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar Classification