•ஶऀ:
•Oisin Mac Aodha, Shihan Su, Yuxin Chen, Pietro Person, Yisong Yue
•California Institute of Technology
•ग़య: CVPR 2018
•ͳΜͰಡΜ͔ͩ?: ࠷৽ͷػցڭࣔΛΩϟονΞοϓ͍͔ͨ͠Β
จใ
• ͳը૾ू߹ ʹରֶͯ͠शऀͷԾઆ มԽ͢Δ
Ծઆ ͷࣄޙ:
ਪ࣌:
T ⊂ X T h
h P(h ∣ T) ∝ P(h)
∏
xt ∈ T
yt ≠ ̂
yh
t
P (y
t
∣ h, x
t)
P (y
t
∣ h, x
t) =
1
1 + exp
(
−αh (xt) yt)
ఏҊख๏
STRICTΞϧΰϦζϜ: Կ͠ͳͱ͖ͷ
ճʹର͢Δ
֬৴
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•ߋ৽ࣜ࣍ͷΑ͏ʹม͑Δ
•৽͘͠2ͭͷݮਰ߲ΛՃ͢Δ
ఏҊख๏
EXPLAINΞϧΰϦζϜ: ϑΟʔυόοΫΛߟ͑Δͱ͖
P(h ∣ T) ∝ P(h)
∏
xt ∈ T
yt ≠ ̂
yh
t
P (y
t
∣ h, x
t)∏
x
t
∈T
(
E (e
t) D (x
t))
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•આ໌ͷ࣭ը૾ͷ͠͞ͱಉ͡Α͏ʹଌΕͳ͍ɽ
•ը૾ͷқఆڥքͱͷڑͰܭࢉͰ͖Δ
•ࣗಈੜ͢Δํ๏͋ͱͰग़ͯ͘ΔΑ
ఏҊख๏
EXPLAINΞϧΰϦζϜ: Modeling Explanations
E (e
t) =
1
1 + exp
(
−β diff (et))
ը૾ ʹର͢Δ༩͑ΒΕͨ
આ໌ ͷ͠͞
x
t
e
t
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•αϯϓϧtͷઆ໌ Λ࡞Γ͍ͨ
•Ϋϥυιʔγϯάͱ͔ઐՈͱ͔ʹͬͯΒ͏ͱ͔͋Δ͚ͲࣗಈͰ࡞
ΕΔͱΑ͘ͳ͍ʁ
•CNNͷClass Activation MappingʹΑͬͯࣗಈͰઆ໌Λ࡞Δ
e
t
ఏҊख๏
EXPLAINΞϧΰϦζϜ: ࣗಈੜ
e(j) =
∑
k
wk
c
fk
j
(x) + b
c
•ैདྷᩦཉʹޡࠩ࠷খΛ࠷దԽ → ඞͣ͠༗ӹͰͳ͍
•ೳಈֶशʹώϯτΛಘͯɼΫϥεͷදྫΛఏࣔ
• ʹͳΔͱSTRICTͱಉ͡ʹͳΔ
β, γ → ∞
ఏҊख๏
EXPLAINΞϧΰϦζϜ: Modeling Representativeness
D (x
t) =
1
1 + exp
(
−γ dist (xt))
ଞͷը૾ͱൺͯ
ͲΕ͘Β͍Ε͍ͯΔ͔
dist (x
t) =
1
N
N
∑
n=1
x
t
− x
n
2
2
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•ڭࡐू߹ ͰͳʹΛબ͢Δ͔ → ֶशऀͷޡࠩΛݮΒ͍ͨ͠
•Ծઆ ʹରͯ͠ɼ؍ଌՄೳͳσʔλͱͷޡࠩΛ࣍ͷΑ͏ʹఆٛ
T
h
ఏҊख๏
Teaching Algorithm: ͲͷαϯϓϧΛఏࣔ͢Δ͔ʁ
err
c
(h) =
x : ( ̂
yh ≠ y
c
∧ y = y
c) ∨ ( ̂
yh = y
c
∧ y ≠ y
c)
| |
.
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•ޡࠩͷظ͕Ұ൪େ͖ܰ͘ݮͰ͖ΔΑ͏ͳू߹Λબ
•͜ͷRΛ࠷େʹ͢ΔΑ͏ͳू߹T͕ཉ͍͠ڭࡐू߹
•͔͠͠ɼٻΊΔͷྼϞδϡϥੑ͔Βࠔ
•ྑ͍αϯϓϧΛ1ͭͣͭՃ͍ͯ͘͠
ఏҊख๏
ڭࡐू߹ͷબ
R(T) =
1
C ∑
c
( [err
c
(h)] − [err
c
(h) ∣ T])
=
1
C ∑
c∈
∑
h∈ℋ
(P
c
(h) − P
c
(h ∣ T)) err
c
(h)
খ͘͞ͳΔ΄Ͳ
خ͍͠
x
t
= argmax
x
R(T ∪ {x})
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•3ͭͷσʔληοτΛ༻͍ͯ༗ޮੑΛ֬ೝ͍ͯ͘͠ɽ
1. Butterflies (ࣝผ)
2. OCT Eyes (ບஅ)
3. Chinese Characters (จࣈࣝผ)
࣮ݧ
σʔληοτ