Hands-On Transfer Learning with Python

Transfer learning – application to the IMDB dataset

One of the scenarios where we should use transfer learning is when we have much less labeled data for the task in hand but lot of training data for a similar but a different domain. The IMDB dataset (http://ai.stanford.edu/~amaas/data/sentiment/) is a binary sentiment classification dataset. It has a set of 25,000 movie reviews for training and 25,000 for testing. There are a lot of published papers on this dataset, and probably the best result on this dataset is achieved by the paragraph vector of Le and Mikolov (https://arxiv.org/pdf/1405.4053.pdf) from Google. They achieved 92.58% accuracy on this dataset. SVM achieved 89% on this. This dataset is of decent size, and we can train the ...