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cs.CV2019
Image to Video Domain Adaptation Using Web Supervision
Andrew Kae, Yale Song
Training deep neural networks typically requires large amounts of labeled data which may be scarce or expensive to obtain for a particular target domain. As an alternative, we can…
cs.CV2018
Learning Embeddings for Product Visual Search with Triplet Loss and Online Sampling
Eric Dodds, Huy Nguyen, Simao Herdade +3
In this paper, we propose learning an embedding function for content-based image retrieval within the e-commerce domain using the triplet loss and an online sampling method that co…
cs.CV2009★ 2 cited
Bounding the Probability of Error for High Precision Recognition
Andrew Kae, Gary B. Huang, Erik Learned-Miller
We consider models for which it is important, early in processing, to estimate some variables with high precision, but perhaps at relatively low rates of recall. If some variables…