Math @ Duke

Publications [#339596] of Guillermo Sapiro
Papers Published
 Qiu, Q; Lezama, J; Bronstein, A; Sapiro, G, ForestHash: Semantic Hashing with Shallow Random Forests and Tiny Convolutional Networks,
Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11206 LNCS
(January, 2018),
pp. 442459, Springer International Publishing [doi]
(last updated on 2019/04/19)
Abstract: © 2018, Springer Nature Switzerland AG. In this paper, we introduce a random forest semantic hashing scheme that embeds tiny convolutional neural networks (CNN) into shallow random forests. A binary hash code for a data point is obtained by a set of decision trees, setting ‘1’ for the visited tree leaf, and ‘0’ for the rest. We propose to first randomly group arriving classes at each tree split node into two groups, obtaining a significantly simplified twoclass classification problem that can be a handled with a lightweight CNN weak learner. Code uniqueness is achieved via the random class grouping, whilst code consistency is achieved using a lowrank loss in the CNN weak learners that encourages intraclass compactness for the two random class groups. Finally, we introduce an informationtheoretic approach for aggregating codes of individual trees into a single hash code, producing a nearoptimal unique hash for each class. The proposed approach significantly outperforms stateoftheart hashing methods for image retrieval tasks on largescale public datasets, and is comparable to image classification methods while utilizing a more compact, efficient and scalable representation. This work proposes a principled and robust procedure to train and deploy in parallel an ensemble of lightweight CNNs, instead of simply going deeper.


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