Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi Proxy Anchor Loss and Effectiveness of Deep Metric Learning
  Performance Metrics   Deep metric learning (DML) learns the mapping, which maps into embedding
space in which similar data is near and dissimilar data is far. However,
conventional proxy-based losses for DML have two problems: gradient problems
and applying the real-world dataset with multiple local centers. Besides, DML
performance metrics also have some issues have stability and flexibility. This
paper proposes multi-proxies anchor (MPA) loss and normalized discounted
cumulative gain (nDCG@k) metric. This study contributes three following: (1)
MPA loss is able to learn the real-world dataset with multi-local centers. (2)
MPA loss improves the training capacity of a neural network owing to solving
the gradient issues. (3) nDCG@k metric encourages complete evaluation for
various datasets. Finally, we demonstrate MPA loss's effectiveness, and MPA
loss achieves higher accuracy on two datasets for fine-grained images.

16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio