1. Generative Model 2. Domain Adaptation 3. Meta-Learning 4. Uncertainty in Deep Learning

1. Recent Trends in
Deep Learning
Sungjoon Choi
CPSLAB, SNU

2. Recent Trends in Deep Learning
2
Generative Model (14) Domain Adaptation (7)
Meta Learning (8) Uncertainty in Deep Learning (6)

3. 3
Generative Model

4. Generative Model
4
What I cannot create, I do not understand

5. Generative Model
5
Generative Adversarial Nets vs. Variational Autoencoders
q
p✓
x
x
zp(z)
z
G
FakeReal
D
True/False
Variational Autoencoder Generative Adversarial Networks

6. Generative Model
6
Generative Adversarial Network (GAN)
IT’S FAKE
MONEY!
IT’S REAL
MONEY!
https://arxiv.org/abs/1406.2661

7. Generative Model
7
Generative Adversarial Network (GAN)
https://arxiv.org/abs/1406.2661

8. Generative Model
8
Variational Autoencoder (VAE)
Kingma’s Thesis

9. Generative Model
9
Variational Autoencoder (VAE)
Kingma’s Thesis

10. Generative Model
10
Variational Autoencoder (VAE)
Kingma’s Thesis

11. Generative Model
11
DCGAN
https://arxiv.org/abs/1511.06434

12. Generative Model
12
Info GAN
https://arxiv.org/abs/1606.03657

13. Generative Model
13
Info GAN
z
G
FakeReal
D
True/False
c
c
https://arxiv.org/abs/1606.03657

14. Generative Model
14
Text2Image
https://arxiv.org/abs/1605.05396

15. Generative Model
15
Text2Image
https://arxiv.org/abs/1605.05396

16. Generative Model
16
Pix2pix
https://arxiv.org/abs/1611.07004

17. Generative Model
17
Pix2pix
https://arxiv.org/abs/1611.07004

18. Generative Model
18
PatchGAN
https://arxiv.org/abs/1604.04382

19. Generative Model
19
Generative Adversarial What-Where Networks (GAWWN)
https://arxiv.org/abs/1610.02454

20. Generative Model
20
Generative Adversarial What-Where Networks (GAWWN)
https://arxiv.org/abs/1610.02454

21. Generative Model
21
Puzzle-GAN
https://arxiv.org/abs/1703.10730

22. Generative Model
22
Puzzle-GAN
https://arxiv.org/abs/1703.10730

23. Generative Model
23
Domain Transfer Network
https://arxiv.org/abs/1611.02200

24. Generative Model
24
DiscoGAN
https://arxiv.org/abs/1703.05192

25. Generative Model
25
DiscoGAN
https://arxiv.org/abs/1703.05192

26. Generative Model
26
CycleGAN
https://arxiv.org/abs/1703.10593

27. Generative Model
27
CycleGAN
https://arxiv.org/abs/1703.10593

28. Generative Model
28
StarGAN
https://arxiv.org/abs/1711.09020

29. Generative Model
29
StarGAN
https://arxiv.org/abs/1711.09020

30. Generative Model
30
Progressive GAN
https://arxiv.org/abs/1710.10196

31. Generative Model
31
Progressive GAN
https://arxiv.org/abs/1710.10196

32. Generative Model
32
Progressive GAN
https://arxiv.org/abs/1710.10196

33. 33
Domain Adaptation

34. Domain Adaptation
34
A step towards practical deployment

35. Domain Adaptation
35
Analysis for domain adaptation
http://www.john.blitzer.com/papers/nips06.pdf

36. Domain Adaptation
36
Domain Adversarial Neural Network (DANN)
https://arxiv.org/abs/1505.07818

37. Domain Adaptation
37
Domain Separation Network (DSN)
https://arxiv.org/abs/1608.06019

38. Domain Adaptation
38
DANN & DSN

39. Domain Adaptation
39
Coupled GAN
https://arxiv.org/abs/1606.07536

40. Domain Adaptation
40
Adversarial Discriminative Domain Adaptation
https://arxiv.org/abs/1606.07536

41. Domain Adaptation
41
Adversarial Discriminative Domain Adaptation
https://arxiv.org/abs/1606.07536

42. Domain Adaptation
42
Appearance adaptation
https://arxiv.org/abs/1703.01461

43. Domain Adaptation
43
Appearance adaptation
Ahttps://arxiv.org/abs/1703.01461

44. Domain Adaptation
44
DA-GAN
https://arxiv.org/abs/1612.05424

45. Domain Adaptation
45
DA-GAN
https://arxiv.org/abs/1612.05424

46. 46
Meta Learning

47. Meta Learning
47
Learn to learn

48. Meta Learning
48
Learning to Learn without Gradient Descent by Gradient Descent
https://arxiv.org/abs/1611.03824

49. Meta Learning
49
Learning to Learn without Gradient Descent by Gradient Descent
https://arxiv.org/abs/1611.03824

50. Meta Learning
50
Siamese Neural Networks
https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf

51. Meta Learning
51
Siamese Neural Networks
https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf

52. Meta Learning
52
Triplet Networks
https://arxiv.org/abs/1412.6622

53. Meta Learning
53
Matching Network
https://arxiv.org/abs/1606.04080

54. Meta Learning
54
Matching Network
https://arxiv.org/abs/1606.04080

55. Meta Learning
55
Prototypical Networks
https://arxiv.org/abs/1703.05175
Prototypical networks learn a metric space in which classification can be performed by
computing distances to prototype representations of each class.

56. Meta Learning
56
Low-shot learning
https://arxiv.org/abs/1606.02819
Low-shot visual learning—the ability to recognize novel object categories from very few
examples—is a hallmark of human visual intelligence.

57. Meta Learning
57
Low-shot learning
https://arxiv.org/abs/1606.02819
Learning to generate new examples

58. Meta Learning
58
Design a CNN with RL
https://arxiv.org/pdf/1611.02167

59. Meta Learning
59
Deep meta RL
https://arxiv.org/abs/1611.05763

60. Meta Learning
60
Deep meta RL
https://arxiv.org/abs/1611.05763

61. Meta Learning
61
Model-Agnostic Meta-Learning
https://arxiv.org/abs/1703.03400

62. Meta Learning
62
Model-Agnostic Meta-Learning
https://arxiv.org/abs/1703.03400

63. Meta Learning
63
Model-Agnostic Meta-Learning
https://arxiv.org/abs/1703.03400
In reinforcement learning (RL), the goal of few-shot meta-learning is to
enable an agent to quickly acquire a policy for a new test task using only
a small amount of experience in the test setting

64. 64
Uncertainty in Deep Learning

65. Uncertainty in Deep Learning
65
What we do not know

66. Uncertainty in Deep Learning
66
What we do not know

67. Uncertainty in Deep Learning
67
What we do not know

68. Uncertainty in Deep Learning
68
What we do not know

69. Uncertainty in Deep Learning
69
Dropout as Bayes Approximation
https://arxiv.org/abs/1506.02142

70. Uncertainty in Deep Learning
70
Dropout as Bayes Approximation
https://arxiv.org/abs/1506.02142

71. Uncertainty in Deep Learning
71
Uncertainty-Aware RL
https://arxiv.org/abs/1702.01182

72. Uncertainty in Deep Learning
72
Uncertainty-Aware RL
https://arxiv.org/abs/1702.01182

73. Uncertainty in Deep Learning
73
Novelty Detection
http://roboticsproceedings.org/rss13/p64.pdf

74. Uncertainty in Deep Learning
74
Novelty Detection
http://roboticsproceedings.org/rss13/p64.pdf

75. Uncertainty in Deep Learning
75
Deep Ensemble
https://arxiv.org/abs/1612.01474
Empirical variance (5) Learning variance (1) Adversarial Ensemble (5)

76. Uncertainty in Deep Learning
76
Deep Ensemble
https://arxiv.org/abs/1612.01474

77. Uncertainty in Deep Learning
77
Epistemic and Aleatoric Uncertainty
https://arxiv.org/abs/1703.04977

78. Uncertainty in Deep Learning
78
Epistemic and Aleatoric Uncertainty
https://arxiv.org/abs/1703.04977

79. Uncertainty in Deep Learning
79
Uncertainty-Aware Learning from Demonstration
https://arxiv.org/abs/1709.02249

80. Uncertainty in Deep Learning
80
Uncertainty-Aware Learning from Demonstration
https://arxiv.org/abs/1709.02249

81. Uncertainty in Deep Learning
81
Uncertainty-Aware Learning from Demonstration
https://arxiv.org/abs/1709.02249

82. Uncertainty in Deep Learning
82
Uncertainty-Aware Learning from Demonstration
https://arxiv.org/abs/1709.02249

83. Uncertainty in Deep Learning
83
Uncertainty-Aware Learning from Demonstration
https://arxiv.org/abs/1709.02249
Unexplained
Variance
Explained
Variance

84. Uncertainty in Deep Learning
84
Uncertainty-Aware Learning from Demonstration
https://arxiv.org/abs/1709.02249
Unexplained
Variance
Explained
Variance

85. Uncertainty in Deep Learning
85
Uncertainty-Aware Learning from Demonstration
https://arxiv.org/abs/1709.02249

86. Conclusion
86
No more ImageNet challenge

87. Conclusion
87
Focus on applications

88. Conclusion
88
Consider real problems

89. 89
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