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08/24/2022
DP2-Pub: Differentially Private High-Dimensional Data Publication with Invariant Post Randomization
A large amount of high-dimensional and heterogeneous data appear in prac...
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01/27/2022
Dissecting the impact of different loss functions with gradient surgery
Pair-wise loss is an approach to metric learning that learns a semantic ...
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08/09/2021
Classification and Visualization of Genotype x Phenotype Interactions in Biomass Sorghum
We introduce a simple approach to understanding the relationship between...
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07/24/2020
Hard negative examples are hard, but useful
Triplet loss is an extremely common approach to distance metric learning...
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10/08/2019
TraffickCam: Explainable Image Matching For Sex Trafficking Investigations
Investigations of sex trafficking sometimes have access to photographs o...
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09/16/2019
Learning Geo-Temporal Image Features
We propose to implicitly learn to extract geo-temporal image features, w...
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09/16/2019
Visualizing How Embeddings Generalize
Deep metric learning is often used to learn an embedding function that c...
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04/08/2019
Improved Embeddings with Easy Positive Triplet Mining
Deep metric learning seeks to define an embedding where semantically sim...
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01/26/2019
Hotels-50K: A Global Hotel Recognition Dataset
Recognizing a hotel from an image of a hotel room is important for human...
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01/02/2019
Visualizing Deep Similarity Networks
For convolutional neural network models that optimize an image embedding...
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08/13/2018
Deep Randomized Ensembles for Metric Learning
Learning embedding functions, which map semantically related inputs to n...
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11/16/2016
Deep Feature Interpolation for Image Content Changes
We propose Deep Feature Interpolation (DFI), a new data-driven baseline ...
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04/15/2013
