A Critical Review of Recurrent Neural Networks for Sequence Learning

Summary: These notes describe the sparse autoencoder learning algorithm, which is one approach to automatically learn features from unlabeled data. In some domains, such as computer vision, this approach is not by itself competitive with the best hand-engineered features, but the features it can learn do turn out to be useful for a range of problems (including ones in audio, text, etc).

Abstract: We present a model that generates natural language descriptions of images and their regions. Our approach leverages datasets of images and their sentence descriptions tolearn about the inter-modal correspondences between language and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate that our alignment model produces state of the art results in retrieval experiments on Flickr8K, Flickr30K and MSCOCOdatasets. We then show that the generated descriptions sig outperform retrieval baselines on both full images and on a new dataset of region-level annotations.