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Table 2 Image search results on the CIFAR-10 dataset for different deep learning hashing methods

From: Deep learning hashing for mobile visual search

ID

Method

12/16-bits

32-bits

48-bits

64-bits

1

Deep learning of binary hash [68]

89.30%

89.72%

89.73%

-

2

Deep hashing for compact binary codes [69]

46.75%

51.01%

-

52.50%

3

Unsupervised deep neural networks hashing [23]

19.43%

24.86%

-

27.73%

4

Supervised deep hashing [22]

46.5%

52.1%

53.2%

-

5

Semantics-preserving deep hashing [23]

-

-

89.97%

-

6

Binary deep neural network hashing [26]

67.32%

69.62%

-

-

7

Bit-scalable deep hashing [27]

55.2%

55.8%

58.1%

-

8

One-stage deep hashing [28]

61.46%

62.87%

63.05%

63.26%

9

Deep pairwise-supervised hashing [29]

71.3%

74.4%

75.7%

-

10

Deep hashing network [72]

55.5%

60.3%

62.1%

-

11

Deep semantic-preserving and ranking-based hashing [73]

≈ 78%

≈ 78%

≈ 77%

-

  1. The accuracy in terms of MAP with different hash bits length, which are collected from their papers