$30 off During Our Annual Pro Sale. View Details »
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
論文紹介 Hardness-Aware Deep Metric Learning [CVPR ...
Search
hyodo
June 10, 2019
Technology
0
530
論文紹介 Hardness-Aware Deep Metric Learning [CVPR 2019]
研究室のゼミで"Deep Metric Learning"というタイトルで発表した資料の一部になります。ご指摘や議論等お待ちしております。
Twitter @onysuke
hyodo
June 10, 2019
Tweet
Share
More Decks by hyodo
See All by hyodo
The Impact of Advertising along the Conversion Funnel
onysuke
2
1.7k
Can offline stores drive online sales?
onysuke
0
1.5k
SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce
onysuke
0
930
意思決定のための機械学習
onysuke
1
1k
Mixture of Expertsに関する文献調査
onysuke
1
2.1k
Other Decks in Technology
See All in Technology
AI with TiDD
shiraji
1
270
さくらのクラウド開発ふりかえり2025
kazeburo
2
1.1k
Amazon Bedrock Knowledge Bases × メタデータ活用で実現する検証可能な RAG 設計
tomoaki25
6
2.3k
Oracle Database@Azure:サービス概要のご紹介
oracle4engineer
PRO
2
200
Knowledge Work の AI Backend
kworkdev
PRO
0
220
AgentCoreとStrandsで社内d払いナレッジボットを作った話
motojimayu
1
900
AWS re:Invent 2025~初参加の成果と学び~
kubomasataka
0
190
New Relic 1 年生の振り返りと Cloud Cost Intelligence について #NRUG
play_inc
0
230
Bedrock AgentCore Evaluationsで学ぶLLM as a judge入門
shichijoyuhi
2
240
TED_modeki_共創ラボ_20251203.pdf
iotcomjpadmin
0
150
日本の AI 開発と世界の潮流 / GenAI Development in Japan
hariby
1
390
AI駆動開発の実践とその未来
eltociear
2
490
Featured
See All Featured
VelocityConf: Rendering Performance Case Studies
addyosmani
333
24k
コードの90%をAIが書く世界で何が待っているのか / What awaits us in a world where 90% of the code is written by AI
rkaga
57
37k
Reflections from 52 weeks, 52 projects
jeffersonlam
355
21k
Why You Should Never Use an ORM
jnunemaker
PRO
61
9.7k
Reality Check: Gamification 10 Years Later
codingconduct
0
1.9k
Organizational Design Perspectives: An Ontology of Organizational Design Elements
kimpetersen
PRO
0
45
Why Mistakes Are the Best Teachers: Turning Failure into a Pathway for Growth
auna
0
28
Performance Is Good for Brains [We Love Speed 2024]
tammyeverts
12
1.4k
How to Build an AI Search Optimization Roadmap - Criteria and Steps to Take #SEOIRL
aleyda
1
1.8k
From π to Pie charts
rasagy
0
91
JAMstack: Web Apps at Ludicrous Speed - All Things Open 2022
reverentgeek
1
290
Building Applications with DynamoDB
mza
96
6.8k
Transcript
)BSEOFTT"XBSF%FFQ.FUSJD-FBSOJOH $7130SBM 8FO[IBP ;IFOH ;IBPEPOH $IFO +JXFO -V +JF ;IPV
%FQBSUNFOUPG"VUPNBUJPO 5TJOHIVB6OJWFSTJUZ $IJOB FUD 1
֓ཁ 2 ɾ/FHBUJWFTBNQMFͷқΛௐ͢ΔϑϨʔϜϫʔΫ )%.- )BSEOFTT"XBSF%FFQ.FUSJD-FBSOJOH ΛఏҊ /FHBUJWFTBNQMFͷқΛજࡏ্ۭؒͷઢܗิؒʹΑΓௐ ֶशঢ়گʹదͳ͠͞ͷOFHBUJWFαϯϓϧΛੜ͢Δ
എܠ • /FHBUJWFTBNQMJOHॏཁͳ • ఏҊ͞Ε͍ͯΔख๏ͷଟ͘ɼֶशΛଅਐ͢Δ ͠ ͍ /FHBUJWFΛͲ͏બ͢Δ͔ʹযΛ͍͋ͯͯͨ ‑ Ұ෦ͷTBNQMFΛऔΓଓ͚Δ͜ͱʹͳΓɼજࡏۭؒͷେ
ہతͳܗΛଊ͑Δ͜ͱ͕Ͱ͖͍ͯͳ͍ PWFSGJUUJOH 3
4 ఏҊख๏֓આ ᶃ )BSEBXBSFGFBUVSFTZOUIFTJT ΞϯΧʔʹ͚ۙͮͨOFHBUJWF ! Λੜ ᶄ )BSEOFTTBOE-BCFM1SFTFSWJOHGFBUVSFTZOUIFTJT
ੜͨ͠OFHBUJWF ! Λ ͷϥϕϧͱಉ͡ʹͳΔΑ͏ʹඍௐ ᶃ ᶄ ! " = "
5 .BOJGPME $MBTT" ఏҊख๏֓આ ᶃ)BSEBXBSFGFBUVSFTZOUIFTJT .BOJGPME $MBTT#
6 .BOJGPME $MBTT" : → GFBUVSFTQBDF͔Β FNCFEEJOHTQBDF NFUSJDTQBDF ʹࣹӨ ఏҊख๏֓આ
ᶃ)BSEBXBSFGFBUVSFTZOUIFTJT .BOJGPME $MBTT#
7 .BOJGPME $MBTT" & ! = + " ! −
" ∈ [0,1] ҎԼͷઢܗิؒʹΑΓ ʹ͚ۙͮͨΑΓ͍͠ ̂ Λੜ ఏҊख๏֓આ ᶃ)BSEBXBSFGFBUVSFTZOUIFTJT .BOJGPME $MBTT#
8 Hard-aware feature .BOJGPME $MBTT" l% !ͱ!͕ಉϥϕϧz อূ͞Ε͍ͯͳ͍ ˣ !ͱಉϥϕϧʹ
ͳΔΑ͏ͳ( !ΛϚοϓ ఏҊख๏֓આ ᶄ)BSEOFTTBOE-BCFM1SFTFSWJOHGFBUVSFTZOUIFTJT : → .BOJGPME $MBTT#
ఏҊϑϨʔϜϫʔΫ )%.- 9 : → : → .FUSJDOFUXPSL "VHNFOUFS HLP(Hardness-and-Label-Preserving)
Generator Network "VHNFOUFS )-1(FOFSBUPS/FUXPSL
"VHNFOUFS 10 : → : → .FUSJDOFUXPSL "VHNFOUPS & !
= + " ! − , "∈ 0,1 … (1) " = + + 1 − # , ! , , ! > # 1 , , ! ≤ # , ∈ 0,1 … (2) ; " ∈ $! $ ," , 1 ͱͯ͠ , ! = ! − ' % ! = + [ , ! + 1 − #] "! $ ," , , ! > # … (3) ' ! = * + [ ! " #!"# , ! + 1 − ! " #!"# $] ! − , ! , , ! > $ ! , , ! ≤ $ … (4) % = 0ͷͱ͖' ! = ͱͳͬͯ͠·͏ʜ ʹ Λೖ͢Δͱ = ! # $%&'ͱͯ͠
"VHNFOUFSֶशঢ়گʹԠͨ͡қͷOFHBUJWFΛੜ ; % # = ' + [ # $
%&'( , # + 1 − # $ %&'( &] # − , # , , # > & # , , # ≤ & … (4) '() ʜͭલͷFQPDIͷ"WFSBHFNFUSJDMPTT FY5SJQMFUMPTT 11 @AB খ େ # $ %&'( 0 1 % ! = + $! $ ," (! − ) % ! = ! % !ͷқ easy hard MPTTͷେ͖͞ ֶशঢ়گ ʹԠͯ͡ੜ͢ΔOFHBUJWFͷқΛௐ
)-1(FOFSBUPS/FUXPSL 12 : → : → "VHNFOUPS HLP(Hardness-and-Label-Preserving) Generator Network
9:; = <:=>; + λ?>@A = − B C + λ?>@A () , ) l% #ͱ#͕ಉϥϕϧzอূ͞Ε͍ͯͳ͍ ⇒ #ͱಉϥϕϧʹͳΔΑ͏ͳE #ΛϚοϓ HFOFSBUPS: → PCKFDUJWFGVODUJPO )-1(FOFSBUPS /FUXPSL &OD %FD ͱͯ͠ͷ੍߲ ݩͷϥϕϧ Λอূ͢Δ
.FUSJDOFUXPSL PCKFDUJWFGVODUJPO .FUSJDOFUXPSL 13 : → : → .FUSJDOFUXPSL "VHNFOUFS
HLP(Hardness-and-Label-Preserving) Generator Network EFGHIJ = ! K L!"#E + 1 − ! K L!"# MNO = ! K L!"#() + 1 − ! K L!"# (; ) NFUSJDMPTT FY5SJQMFUMPTT /QBJSMPTT ݩͷσʔλର ੜͨ͠σʔλର ৴པͰ͖Δ 㱺 ੜͨ͠σʔλର ৴པͰ͖ͳ͍ 㱺 ݩͷσʔλର HFOFSBUPS ͕ ͷNFUSJDMPTTʹॏ͖Λ͓͘
$6#σʔληοτ ௗͷը૾ छྨ ܭ ຕ 5SBJO ຕ छྨ 5FTU
ຕ छྨ 5SBJOͱ5FTUʹಉ͡Ϋϥεͷը૾ଘࡏ͠ͳ͍ 㱺 ;FSPTIPUTFUUJOH 14
࣮ݧઃఆ DMVTUFSJOHSFUSJFWBMUBTL 15 $MVTUFSJOHUBTL ධՁࢦඪ /.* ਖ਼نԽ૬ޓใྔ ' 3FDBMM!, 5FTU
5SBJO Clustering task Retrieval task 3FUSJFWBMUBTL ֤UFTUը૾ RVFSZ ʹରͯ͠ ,ίۙͷΛநग़͠ɼ ಉ͡Ϋϥε͕ଐ͍ͯ͠Ε TDPSFFMTFTDPSF
.FUSJDMPTTͷछྨʹΑΒͣ )%.-Ͱࣝผతͳಛྔ͕ಘΒΕͨ 16
!"#$ ֶ͕शʹ͓͍ͯॏཁͳཁૉͰ͋Δ 17 HFJQO ͳ͠ͰϕʔεϥΠϯΛ্ճΔ 㱺 *+,- ͚ͩͰݱ࣮తͳಛදݱͷϚοϐϯά͕ՄೳͰ͋ͬͨͱߟ͑ΒΕΔ
ΫϥεͷมԽ എܠ ࢹ র໌ FUD ΫϥεؒͷΘ͔ͣͳҧ͍ ௗͷ༷ 18 ʹରॲ