Timezone: »
Fast retrieval methods are increasingly critical for many large-scale analysis tasks, and there have been several recent methods that attempt to learn hash functions for fast and accurate nearest neighbor searches. In this paper, we develop an algorithm for learning hash functions based on explicitly minimizing the reconstruction error between the original distances and the Hamming distances of the corresponding binary embeddings. We develop a scalable coordinate-descent algorithm for our proposed hashing objective that is able to efficiently learn hash functions in a variety of settings. Unlike existing methods such as semantic hashing and spectral hashing, our method is easily kernelized and does not require restrictive assumptions about the underlying distribution of the data. We present results over several domains to demonstrate that our method outperforms existing state-of-the-art techniques.
Author Information
Brian Kulis (UC Berkeley)
Trevor Darrell (UC Berkeley)
Related Events (a corresponding poster, oral, or spotlight)
-
2009 Poster: Learning to Hash with Binary Reconstructive Embeddings »
Thu Dec 10th 03:00 -- 07:59 AM Room None
More from the Same Authors
-
2020 Poster: Auxiliary Task Reweighting for Minimum-data Learning »
Baifeng Shi · Judy Hoffman · Kate Saenko · Trevor Darrell · Huijuan Xu -
2020 Poster: Fighting Copycat Agents in Behavioral Cloning from Observation Histories »
Chuan Wen · Jierui Lin · Trevor Darrell · Dinesh Jayaraman · Yang Gao -
2019 Workshop: AI for Humanitarian Assistance and Disaster Response »
Ritwik Gupta · Robin Murphy · Trevor Darrell · Eric Heim · Zhangyang Wang · Bryce Goodman · Piotr BiliĆski -
2019 Poster: Compositional Plan Vectors »
Coline Devin · Daniel Geng · Pieter Abbeel · Trevor Darrell · Sergey Levine -
2019 Poster: Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity »
Deepak Pathak · Christopher Lu · Trevor Darrell · Phillip Isola · Alexei Efros -
2019 Spotlight: Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity »
Deepak Pathak · Christopher Lu · Trevor Darrell · Phillip Isola · Alexei Efros -
2018 Poster: Speaker-Follower Models for Vision-and-Language Navigation »
Daniel Fried · Ronghang Hu · Volkan Cirik · Anna Rohrbach · Jacob Andreas · Louis-Philippe Morency · Taylor Berg-Kirkpatrick · Kate Saenko · Dan Klein · Trevor Darrell -
2017 Poster: Toward Multimodal Image-to-Image Translation »
Jun-Yan Zhu · Richard Zhang · Deepak Pathak · Trevor Darrell · Alexei Efros · Oliver Wang · Eli Shechtman -
2016 Workshop: Machine Learning for Intelligent Transportation Systems »
Li Erran Li · Trevor Darrell -
2014 Poster: Do Convnets Learn Correspondence? »
Jonathan L Long · Ning Zhang · Trevor Darrell -
2014 Poster: LSDA: Large Scale Detection through Adaptation »
Judy Hoffman · Sergio Guadarrama · Eric Tzeng · Ronghang Hu · Jeff Donahue · Ross Girshick · Trevor Darrell · Kate Saenko -
2014 Poster: Weakly-supervised Discovery of Visual Pattern Configurations »
Hyun Oh Song · Yong Jae Lee · Stefanie Jegelka · Trevor Darrell -
2013 Poster: Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies »
Yangqing Jia · Joshua T Abbott · Joseph L Austerweil · Tom Griffiths · Trevor Darrell -
2012 Poster: Learning with Recursive Perceptual Representations »
Oriol Vinyals · Yangqing Jia · Li Deng · Trevor Darrell -
2012 Poster: Timely Object Recognition »
Sergey K Karayev · Tobi Baumgartner · Mario Fritz · Trevor Darrell -
2011 Workshop: Integrating Language and Vision »
Raymond Mooney · Trevor Darrell · Kate Saenko -
2011 Workshop: Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity »
Greg Shakhnarovich · Dhruv Batra · Brian Kulis · Kilian Q Weinberger -
2011 Poster: Heavy-tailed Distances for Gradient Based Image Descriptors »
Yangqing Jia · Trevor Darrell -
2010 Poster: Factorized Latent Spaces with Structured Sparsity »
Yangqing Jia · Mathieu Salzmann · Trevor Darrell -
2010 Poster: Size Matters: Metric Visual Search Constraints from Monocular Metadata »
Mario J Fritz · Kate Saenko · Trevor Darrell -
2009 Poster: An Additive Latent Feature Model for Transparent Object Recognition »
Mario J Fritz · Michael J Black · Gary R Bradski · Trevor Darrell -
2009 Poster: Filtering Abstract Senses From Image Search Results »
Kate Saenko · Trevor Darrell -
2009 Oral: An Additive Latent Feature Model for Transparent Object Recognition »
Mario J Fritz · Michael J Black · Gary R Bradski · Trevor Darrell -
2008 Poster: Unsupervised Learning of Visual Sense Models for Polysemous Words »
Kate Saenko · Trevor Darrell -
2008 Spotlight: Unsupervised Learning of Visual Sense Models for Polysemous Words »
Kate Saenko · Trevor Darrell -
2006 Poster: Approximate Correspondences in High Dimensions »
Kristen Grauman · Trevor Darrell -
2006 Spotlight: Approximate Correspondences in High Dimensions »
Kristen Grauman · Trevor Darrell