17 citations · 30 across the 7 of their papers we have counts for
3 papers · 1 filter
Improving Calibration in Deep Metric Learning With Cross-Example Softmax
Andreas Veit, Kimberly Wilber
Modern image retrieval systems increasingly rely on the use of deep neural networks to learn embedding spaces in which distance encodes the relevance between a given query and imag…
Coping with Label Shift via Distributionally Robust Optimisation
Jingzhao Zhang, Aditya Menon, Andreas Veit +3
The label shift problem refers to the supervised learning setting where the train and test label distributions do not match. Existing work addressing label shift usually assumes ac…
Doubly-stochastic mining for heterogeneous retrieval
Ankit Singh Rawat, Aditya Krishna Menon, Andreas Veit +3
Modern retrieval problems are characterised by training sets with potentially billions of labels, and heterogeneous data distributions across subpopulations (e.g., users of a retri…