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Adaptive neighborhood Metric learning. (arXiv:2201.08314v1 [cs.LG])
Jan. 21, 2022, 2:10 a.m. | Kun Song, Junwei Han, Gong Cheng, Jiwen Lu, Feiping Nie
cs.LG updates on arXiv.org arxiv.org
In this paper, we reveal that metric learning would suffer from serious
inseparable problem if without informative sample mining. Since the inseparable
samples are often mixed with hard samples, current informative sample mining
strategies used to deal with inseparable problem may bring up some
side-effects, such as instability of objective function, etc. To alleviate this
problem, we propose a novel distance metric learning algorithm, named adaptive
neighborhood metric learning (ANML). In ANML, we design two thresholds to
adaptively identify the …
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