Oct 19, 2020 · In this paper, we propose a new method on how to optimize top-k precision in a rank-sensitive manner. Given the cutoff value k, our key idea is to impose ...
Oct 23, 2020 · In this paper, we propose a new method on how to optimize top-k precision in a rank-sensitive manner. Given the cutoff value k, our key idea is ...
In this paper we study the stability and its trade-off with optimization error for stochastic gradient descent (SGD) algorithms in the pairwise learning ...
Deep Metric Learning Based on Rank-sensitive Optimization of Top-k Precision. https://doi.org/10.1145/3340531.3412142. Journal: Proceedings of the 29th ACM ...
Deep Metric Learning Based on Rank-sensitive Optimization of Top-k Precision. Naoki Muramoto, Hai-Tao Yu. Anthology ID: DBLP:conf/cikm/MuramotoY20; Volume: ...
... TopK-Pre outperforms TopK-Pre. It shows the potential value of rank-sensitive optimization of top-k precision for deep metric learning with continuous labels.
We propose a novel deep metric learning method by re- visiting the learning to rank approach. Our method, named. FastAP, optimizes the rank-based Average ...
In this thesis, we proposed novel methods on how to optimize top-k precision in a rank-sensitive manner for deep metric learning. Given the cutoff value k, our ...
Missing: Optimization | Show results with:Optimization
Naoki Muramoto, Hai-Tao Yu: Deep Metric Learning Based on Rank-sensitive Optimization of Top-k Precision. CIKM 2020: 2161-2164.
Deep Metric Learning Based on Rank-sensitive Optimization of Top-k Precision. DOI Web Site 8 References. Naoki Muramoto: University of Tsukuba, Tsukuba, Japan.
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