Representation learning of recipes

This work introduces methods for learning distributed, vector representations of cooking recipes. The individual components of a recipe -- the images, instructions, and ingredients -- are first treated individually. These representations are learned from a large, multi-modal dataset collected -- and publicly released -- as part of this work. Their representations are then embedded in a joint vector space using a novel neural network model. Experiments on cross-modal retrieval and vector space arithmetic demonstrate the utility and generalizability of both the per-component and joint embeddings.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Hynes, Nick (Nick I.)

Advisor dc:contributor.advisor

• Antonio Torralba.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/113147

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/113147