Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Pathologies of Neural Models Make Interpretatio...
Search
Yasufumi Taniguchi
December 09, 2018
Research
1
1.8k
Pathologies of Neural Models Make Interpretations Difficult
Yasufumi Taniguchi
December 09, 2018
Tweet
Share
More Decks by Yasufumi Taniguchi
See All by Yasufumi Taniguchi
AllenNLPを使った開発
yasufumy
0
2.2k
Making Neural QA as Simple as Possible but not Simpler
yasufumy
0
97
Other Decks in Research
See All in Research
EcoWikiRS: Learning Ecological Representation of Satellite Images from Weak Supervision with Species Observation and Wikipedia
satai
3
140
AWSで実現した大規模日本語VLM学習用データセット "MOMIJI" 構築パイプライン/buiding-momiji
studio_graph
2
520
AI エージェントを活用した研究再現性の自動定量評価 / scisci2025
upura
1
150
2025年度人工知能学会全国大会チュートリアル講演「深層基盤モデルの数理」
taiji_suzuki
25
18k
Learning to (Learn at Test Time): RNNs with Expressive Hidden States
kurita
0
170
20250725-bet-ai-day
cipepser
2
420
SSII2025 [TS1] 光学・物理原理に基づく深層画像生成
ssii
PRO
4
4.2k
不確実性下における目的と手段の統合的探索に向けた連続腕バンディットの応用 / iot70_gp_rff_mab
monochromegane
2
170
20250624_熊本経済同友会6月例会講演
trafficbrain
1
620
Stealing LUKS Keys via TPM and UUID Spoofing in 10 Minutes - BSides 2025
anykeyshik
0
120
まずはここから:Overleaf共同執筆・CopilotでAIコーディング入門・Codespacesで独立環境
matsui_528
2
500
Adaptive Experimental Design for Efficient Average Treatment Effect Estimation and Treatment Choice
masakat0
0
110
Featured
See All Featured
Scaling GitHub
holman
463
140k
The Cost Of JavaScript in 2023
addyosmani
53
8.9k
Building Better People: How to give real-time feedback that sticks.
wjessup
368
19k
Sharpening the Axe: The Primacy of Toolmaking
bcantrill
44
2.5k
The Illustrated Children's Guide to Kubernetes
chrisshort
48
50k
Making the Leap to Tech Lead
cromwellryan
135
9.5k
Building a Modern Day E-commerce SEO Strategy
aleyda
43
7.6k
The Power of CSS Pseudo Elements
geoffreycrofte
77
6k
The Language of Interfaces
destraynor
161
25k
Fashionably flexible responsive web design (full day workshop)
malarkey
407
66k
Rebuilding a faster, lazier Slack
samanthasiow
83
9.2k
A better future with KSS
kneath
239
17k
Transcript
ൃදऀ ୩ޱହ࢙ ҟৗͳڍಈ
!2 Pathological behavior ࣭จ͕did͚ͩͰ Ϟσϧͷग़ྗಉ͡ ֬ߴ͍
֓ཁ w NLPʹ͓͚ΔχϡʔϥϧϞσϧͷղੳख๏ΛఏҊ w Ϟσϧ͕λεΫΛղ্͘Ͱॏཁͳ୯ޠΛநग़͢Δख๏ w நग़͞Εͨ୯ޠਓʹͱͬͯҙຯෆ໌ w ҰํͰϞσϧநग़୯ޠͰਖ਼͘͠༧ଌ(Pathology) w
ղੳ݁Ռʹجͮ͘ਖ਼ଇԽ߲ΛఏҊ w ਖ਼ଇԽ߲ʹΑͬͯϞσϧͷղऍੑ্ !3
࣍ Ϟσϧղੳͷطଘख๏ ఏҊख๏ ࣮ݧ ·ͱΊ !4
Ϟσϧղੳͷطଘख๏
Ϟσϧղੳͷطଘख๏ !6 Adversarial Example Ϟσϧʹਓͷײʹ͢ΔڍಈΛͤ͞Δαϯϓϧ NLPͷλεΫ ओʹQAλεΫ Ͱύλʔϯ ਓʹͱͬͯҙຯͷͳ͍มߋ͕ɺϞσϧͷग़ྗΛܹมͤ͞Δέʔε
ਓʹͱͬͯ໌Β͔ͳมߋͰɺϞσϧ͕ग़ྗΛม͑ͳ͍έʔε
ग़ྗ͕ܹม͢Δέʔε !7 Jia et al., 2017 ΫΥʔλʔόοΫͷྸʹ͍ͭͯͷ จॻʹΫΥʔλʔόοΫͷഎ൪߸ʹ ؔ͢ΔจΛՃ Ϟσϧޡ
ग़ྗΛม͑ͳ͍έʔε !8 Mudrakarta et al., 2018 ݐͷന͍ϨϯΨ͕ରশ͔ʁ spherical (ٿঢ়ͷ) ݐͷന͍ϨϯΨ͕ٿঢ়͔ʁ
࣭จͷҙຯมԽ Ϟσϧͷ༧ଌෆม
2. ఏҊख๏
*OQVU3FEVDUJPO • ॏཁͰͳ͍୯ޠΛೖྗ͔ΒΓɺϞσϧͷڍಈΛੳ • Ϟσϧ͕ਖ਼͍͠ग़ྗΛ͢ΔͨΊʹඞཁͳ࠷୯ޠ (ॏཁ ୯ޠ) • Adversarial ExampleϞσϧʹͱͬͯͷॏཁ୯ޠʹண
*OQVU3FEVDUJPO !11 x y Ϟσϧͷ༧ଌ f( ⋅ ) χϡʔϥϧϞσϧ ೖྗܥྻ
(จจॻ) xi ೖྗܥྻͷ͋Δཁૉ (୯ޠ) g(xi |x) = f(y|x) − f(y|x−i ) ͋Δ୯ޠ ʹର͢Δ ॏཁΛఆٛ xi g i൪ͷ୯ޠΛফͨ͠ೖྗ
*OQVU3FEVDUJPO !12 g(xcontest |x) = f(y|x) − f(y|x−contest ) What
company won free advertisement due to QuickBooks contest ? What company won free advertisement due to QuickBooks contest ? g͕େ͖͚Εɺcontest͕ॏཁͳ୯ޠͱͳΔ Ϟσϧͷग़ྗʹେ͖͘د༩͍ͯ͠ΔͨΊ
*OQVU3FEVDUJPO !13 g(xi |x) = f(y|x) − f(y|x−i ) ॏཁͷ͍୯ޠΛআ
y͕มԽ͠ͳ͍Α͏ʹɺg͕࠷খͱͳΔ୯ޠiΛআ ͍ͯ͘͠
3. ࣮ݧ
ղੳͷରλεΫ 1. SQuAD w จॻͱ࣭จ͕༩͑ΒΕΔˠ࣭จʹରͯ͠Input Reduction w จॻ͔ΒղΛநग़͢ΔλεΫ 2. SNLI
w จ͕༩͑ΒΕΔˠͭͷจʹରͯ͠Input Reduction w จͷؔΛਪఆ͢ΔλεΫ 3. VQA w ը૾ͱ࣭จ͕༩͑ΒΕΔˠ࣭จʹରͯ͠Input Reduction w ղΛੜ͢ΔλεΫ !15
࣮ݧ༰ Input Reduction w Ϟσϧ͕ਖ਼͍͠ग़ྗΛ͢ΔαϯϓϧΛରʹ࣮ݧ w Input ReductionΛద༻ͨ͠ೖྗ(Reduced)ʹର͢ΔਓखධՁ w ReducedͱϥϯμϜʹ୯ޠΛམͱͨ͠߹(Random)ͷࠩҟͷධՁ
Regularization on Reduced Inputs w Input ReductionʹΑΔϞσϧͷPathological behaviorΛܰݮ͢Δਖ਼ଇԽ߲ ޙड़ ͷಋೖ !16
Reducedʹର͢ΔਓखධՁ !17 Reducedʹରͯ͠ ਓਖ਼͍͠༧ଌΛͰ ͖ͳ͍ w Reducedʹର͢Δਓͷਖ਼ w Ϟσϧͷਖ਼͕ͷαϯϓϧΛ༻
Reducedʹର͢ΔਓखධՁ !18 w ReducedͱRandomͷͲͪΒ͕ࣗવͳจ͔ w vs. Randomfifty-fiftyͱׂ͑ͨ߹ Reducedਓʹͱͬ ͯRandomͱಉ͡
Reducedͷࣄྫ !19 ʮͲ͜Ͱ࿅शͨ͠ ͔ʯΛฉ͔Ε͍ͯ ΔͷΘ͔Δ͕ɺ ʮͲͷνʔϜʯ͔ Θ͔Βͳ͍
Reducedͷฏۉ୯ޠ ͭͷλεΫͱɺ ਖ਼͢Δͷʹඞཁͳ୯ޠฏۉd
Reducedʹର͢ΔϞσϧͷ֬ !21 • Input Reductionͷద༻લޙͰϞσϧͷ ֬ʹมԽ΄ͱΜͲͳ͍ • ϞσϧӶ͍ϐʔΫΛ࣋ͭΑ͏ͳ Λֶश͍ͯ͠Δ͜ͱ͕ݪҼ
ਖ਼ଇԽ߲ͷಋೖ !22 ∑ (x,y)∈(X,Y) log(f(y|x)) + λ∑ ¯ x∈ ¯
X H(f(y| ¯ x)) Reducedʹରͯ͠ਖ਼͍͠yΛ ग़ྗ͠ʹ͘͘͢Δ ௨ৗͷతؔ Reducedαϯϓϧ௨ৗͷతؔΛֶͬͯशͨ͠ ϞσϧΛ༻͍ͯੜ
ਖ਼ଇԽ߲ͷޮՌ !23 • Ϟσϧͷਫ਼͕ඍ૿ • ਖ਼ʹඞཁͳ୯ޠ ͕૿Ճ
ਖ਼ଇԽ߲ͷޮՌ !24 ਓखධՁͷਫ਼্ Input Reductionͨ͠ೖྗ ͷղऍੑ্͕
ਖ਼ଇԽͨ͠Ϟσϧͷࣄྫ !25 Input Reductionͨ͠ೖྗ͕ਓͰ ղऍՄೳʹͳͬͨ
·ͱΊ ఏҊख๏ w NLPͷχϡʔϥϧϞσϧղੳख๏ͱͯ͠ɺInput ReductionΛఏҊ w ༧ଌʹد༩͠ͳ͍୯ޠΛೖྗ͔ΒΓɺϞσϧΛղੳ ࣮ݧ݁Ռ w ఏҊख๏Λద༻ͨ͠ೖྗਓʹͱͬͯҙຯෆ໌
w ҰํͰχϡʔϥϧϞσϧਖ਼͍͠༧ଌΛߦ͏ w ਖ਼ଇԽ߲Λಋೖ͢ΔͱϞσϧͷڍಈվળ !26