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모바일 얼굴인식 SDK 개발

kakao
PRO
December 08, 2022

모바일 얼굴인식 SDK 개발

#FacialRecognition

딥러닝 기반의 얼굴인식 모델을 처리 성능이 빈약한 모바일 및 전용장비에 적용하기 위한 최적화 방법에 대해서 공유하고자 합니다.

발표자 : andrew.js
카카오엔터프라이즈 비전인텔리전스팀의 앤드루와 포입니다. 얼굴인식을 모바일에 적용하는 일을 하고 있습니다.

po.ai
카카오엔터프라이즈 비전인텔리전스팀의 앤드루와 포입니다. 얼굴인식을 모바일에 적용하는 일을 하고 있습니다.

kakao
PRO

December 08, 2022
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  1. $PQZSJHIU ,BLBP $PSQ"MMSJHIUTSFTFSWFE3FEJTUSJCVUJPOPSQVCMJDEJTQMBZJTOPUQFSNJUUFEXJUIPVUXSJUUFOQFSNJTTJPOGSPN,BLBP
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    ஠஠য় ূఠ೐ۄ੉ૉ
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  2. ঴ҷ ੋध 4%,ࣗѐ +BOVT4%,

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  3. ,BLBP&OUFSQSJTFࢎޖप ਕ௼झܖ

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  4. ݽ߄ੌ ঴ҷੋध 4%,੄ ೙ਃࢿ
    ҃۝ച ߂ ࢿמ ӓ؀ച ೙ਃ
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    ޷Ҵ Ҵ݀಴ળӝࣿোҳࣗ /*45
    ੄ ঴ҷ ੋध ߮؊ ߮஖݃௼ పझ౟

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  5. ݽ߄ੌ ঴ҷੋध 4%,੄ ೙ਃࢿ
    ׮নೠ ೒ۖಬ ߂ ٣߄੉झী ੸ਊ оמ
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  6. ݽ߄ੌ ঴ҷੋध 4%,੄ ೙ਃࢿ
    ׮নೠ ೒ۖಬ ߂ ٣߄੉झী ੸ਊ оמ
    п ٣߄੉झо о૑Ҋ ੓ח ୭Ҋ੄ ࢿמਵ۽ ز੘ ೧ঠ ೠ׮
    ৈ۞ ٣߄੉झܳ ഒ೤ ਍৔੉ оמೞ׮
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  7. ઱ਃ ٣߄੉झ߹ प೯ ࣘب
    GQT੿بܳ ࢲ࠺झী ਗഝೠ ࣘب۽ ࠅ ࣻ ੓णפ׮
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    ਬפৡழޭפ౭
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    GQT

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  8. ઱ਃ ٣߄੉झ߹ प೯ ࣘب
    GQT੿بܳ ࢲ࠺झী ਗഝೠ ࣘب۽ ࠅ ࣻ ੓णפ׮
    ࢖ࢿ੹੗
    (BMBYZ5BC 4
    GQT

    ਬפৡழޭפ౭
    6#JP9'BDF1SFNJVN
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    GQT

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  9. ࢲߡ "1*
    ױ݈ "1*
    4%,о ૑ਗ ೞח 1MBUGPSN
    $$PSF-JC
    "OESPJE
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    J04
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    1ZUIPO
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    +BWB ,PUMJO
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    ݽ߄ੌҗ ࢲߡജ҃ী ೙ਃೠ -JCSBSZઁҕ
    ݽ߄ੌҗ ࢲߡ জ ࢚ഐ ਍৔੉ ੌ߈੸ ੉׮
    ݽ߄ੌ জ ױة ਍৔ ҳࢿ оמ
    ࢲߡ জ ױة ਍৔ ҳࢿ оמ
    ݽ߄ੌ জ
    .PCJMF"QQ

    ࢲߡ জ
    4FSWFS"QQ

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  10. ࢲߡ "1*
    ױ݈ "1*
    4%,о ૑ਗ ೞח 1MBUGPSN
    $$PSF-JC
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    J04
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    ݽ߄ੌҗ ࢲߡജ҃ী ೙ਃೠ -JCSBSZઁҕ
    ݽ߄ੌҗ ࢲߡ জ ࢚ഐ ਍৔੉ ੌ߈੸ ੉׮
    ݽ߄ੌ জ ױة ਍৔ ҳࢿ оמ
    ࢲߡ জ ױة ਍৔ ҳࢿ оמ
    ݽ߄ੌ জ
    .PCJMF"QQ

    ࢲߡ জ
    4FSWFS"QQ

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  11. ࢲߡ "1*
    ױ݈ "1*
    4%,о ૑ਗ ೞח 1MBUGPSN
    $$PSF-JC
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    ݽ߄ੌҗ ࢲߡജ҃ী ೙ਃೠ -JCSBSZઁҕ
    ݽ߄ੌҗ ࢲߡ জ ࢚ഐ ਍৔੉ ੌ߈੸ ੉׮
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    ࢲߡ জ
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  12. 1MBUGPSN-FWFMীࢲ੄ ബਯച ౲

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  13. ঴ҷ ੋध 4%,੄ ݽٕ 1JQFMJOF

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  14. ঴ҷ ੋध 4%,੄ ݽٕ 1JQFMJOF

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  15. ٩۞׬ ೐ۨ੐ਕ௼ %- 'SBNFXPSL

    1ZUPSDI
    0//9
    ,FSBT
    5FOTPSGMPX-JUF
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    ݽ؛ ѐߊ ױ҅ ߓನ ױ҅
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  16. ٩۞׬ ೐ۨ੐ਕ௼ %- 'SBNFXPSL

    1ZUPSDI
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    ݽ؛ ѐߊ ױ҅ ߓನ ױ҅
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  17. ٩۞׬ ೐ۨ੐ਕ௼ %- 'SBNFXPSL

    1ZUPSDI
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    ݽ؛ ѐߊ ױ҅ ߓನ ױ҅
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  18. 5FOTPSGMPX-JUFܳ ݽ߄ੌ ೐ۨ੐ਕ௼۽ ࢶఖೠ ੉ਬ
    -BUFODZ ࢿמ੉ જ׮
    ׮নೠ ߔূ٘ #BDLFOE
    ૑ਗ
    DQV HQV OOBQJ YOOQBDL DPSFNM FUD
    "SNӝ߈ Gীࢲח YOOQBDLӝ߈ CBDLFOEо જ਷ ࢿמ
    ୭Ӕ੄ %FWJDFীࢲ HQV ӝ߈ ࢿמ੉ ੼੼ જই ૑Ҋ੓׮
    //"1* /FVSBM/FUXPSL"1*
    ח উ٘۽੉٘੄ ಴ળ ݠन۞׬ झಖ੉׮
    ࢚؀੸ਵ۽ ߊৌী ஂডೞ૑ ঋ׮
    উ੿੸ੋ ন੗ച 2VBOUJ[BUJPO
    ૑ਗ
    ׮নೠ ೒ۖಬ J04 "OESPJE 4FSWFS
    ૑ਗ
    ٩۞׬ ೐ۨ੐ਕ௼ %- 'SBNFXPSL

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  19. 5FOTPSGMPX-JUFܳ ݽ߄ੌ ೐ۨ੐ਕ௼۽ ࢶఖೠ ੉ਬ
    -BUFODZ ࢿמ੉ જ׮
    ׮নೠ ߔূ٘ #BDLFOE
    ૑ਗ
    DQV HQV OOBQJ YOOQBDL DPSFNM FUD
    "SNӝ߈ Gীࢲח YOOQBDLӝ߈ CBDLFOEо જ਷ ࢿמ
    ୭Ӕ੄ %FWJDFীࢲ HQV ӝ߈ ࢿמ੉ ੼੼ જই ૑Ҋ੓׮
    //"1* /FVSBM/FUXPSL"1*
    ח উ٘۽੉٘੄ ಴ળ ݠन۞׬ झಖ੉׮
    ࢚؀੸ਵ۽ ߊৌী ஂডೞ૑ ঋ׮
    উ੿੸ੋ ন੗ച 2VBOUJ[BUJPO
    ૑ਗ
    ׮নೠ ೒ۖಬ J04 "OESPJE 4FSWFS
    ૑ਗ
    ٩۞׬ ೐ۨ੐ਕ௼ %- 'SBNFXPSL

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  20. 5FOTPSGMPX-JUFܳ ݽ߄ੌ ೐ۨ੐ਕ௼۽ ࢶఖೠ ੉ਬ
    -BUFODZ ࢿמ੉ જ׮
    ׮নೠ ߔূ٘ #BDLFOE
    ૑ਗ
    DQV HQV OOBQJ YOOQBDL DPSFNM FUD
    "SNӝ߈ Gীࢲח YOOQBDLӝ߈ CBDLFOEо જ਷ ࢿמ
    ୭Ӕ੄ %FWJDFীࢲ HQV ӝ߈ ࢿמ੉ ੼੼ જই ૑Ҋ੓׮
    //"1* /FVSBM/FUXPSL"1*
    ח উ٘۽੉٘੄ ಴ળ ݠन۞׬ झಖ੉׮
    ࢚؀੸ਵ۽ ߊৌী ஂডೞ૑ ঋ׮
    উ੿੸ੋ ন੗ച 2VBOUJ[BUJPO
    ૑ਗ
    ׮নೠ ೒ۖಬ J04 "OESPJE 4FSWFS
    ૑ਗ
    ٩۞׬ ೐ۨ੐ਕ௼ %- 'SBNFXPSL

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  21. %- ݽ؛੄ ѐࣻܳ ઴ৈۄ
    ੋध /FUXPSLҗ ݃झ௼ Ѩ୹ /FUXPSLਸ ా೤
    Ѣ੄ ݃झ௼ Ѩ୹ /FUXPSL ݅ఀ੄ -BUFODZਸ ੺ড ೡ ࣻ ੓঻׮
    ୶о ௏झ౟о Ѣ੄ হ੉ ׮ܲ ࣘࢿٜب ୶о ೡ ࣻ ੓঻׮

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  22. %- ݽ؛੄ ѐࣻܳ ઴ৈۄ
    ੋध /FUXPSLҗ ݃झ௼ Ѩ୹ /FUXPSLਸ ా೤
    Ѣ੄ ݃झ௼ Ѩ୹ /FUXPSL ݅ఀ੄ -BUFODZਸ ੺ড ೡ ࣻ ੓঻׮
    ୶о ௏झ౟о Ѣ੄ হ੉ ׮ܲ ࣘࢿٜب ୶о ೡ ࣻ ੓঻׮

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  23. /&0/ਸ ੸ӓ੸ਵ۽ ഝਊೞۄ
    /&0/਷ "SN੉ ૑ਗೞח 4*.% 4JOHMF*OTUSVDUJPO.VMUJQMF%BUB

    2
    2
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    4PVSDF
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    7"%%2G

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  24. /&0/ਸ ੉޷૑ ୊ܻ *NBHF1SPDFTTJOH
    ী ੸ਊ
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    (BMBYZ4 6CJP9
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    /&0/റ N4 N4
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    3PUBUF'MJQ
    (BMBYZ4 6CJP9
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    റ N4 N4
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    ੹ N4 N4
    റ N4 N4
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    റ N4 N4
    ౠ૚ ࢤࢿӝ
    ੹ N4 N4
    റ N4 N4
    1JYFM/PSNBMJ[BUJPO

    à
    PS

    N4ب ࣗ઺೤פ׮

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  25. /&0/ਸ ੉޷૑ ୊ܻ *NBHF1SPDFTTJOH
    ী ੸ਊ
    Image(YUV) Image(RGB)
    (BMBYZ4 6CJP9
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    ঴ҷ ৔৉ Ѩ୹ӝ
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    റ N4 N4
    ঴ҷ ਤઑ х૑ӝ
    ੹ N4 N4
    റ N4 N4
    ౠ૚ ࢤࢿӝ
    ੹ N4 N4
    റ N4 N4
    1JYFM/PSNBMJ[BUJPO

    à
    PS

    N4ب ࣗ઺೤פ׮

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  26. 1JYFM/PSNBMJ[BUJPO୭੸ച
    ా೤ оמೠ ো࢑ਸ ޷ܻ ࣻ೯ 0GGMJOF

    #BUDI /PSNBMJ[BUJPO-BZFS੄ GVTJOHҗ ਬࢎ
    2VBOUJ[BUJPOਸ Ҋ۰ೞৈ ࣗࣻ੼੉ ߊࢤೞ૑ ঋח NFBO
    TUE
    ਸ ࢎਊೣ

    ޷

    QJYFM@NFBO
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    QJYFM@NFBO
    ࡐӝ
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    ޷

    ೟ण җ੿ 5SBJOJOH5JNF

    ѐࢶ ੹ *OGFSFODF5JNF
    ѐࢶ റ *OGFSFODF5JNF

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  27. 1JYFM/PSNBMJ[BUJPO୭੸ച
    ా೤ оמೠ ো࢑ਸ ޷ܻ ࣻ೯ 0GGMJOF

    #BUDI /PSNBMJ[BUJPO-BZFS੄ GVTJOHҗ ਬࢎ
    2VBOUJ[BUJPOਸ Ҋ۰ೞৈ ࣗࣻ੼੉ ߊࢤೞ૑ ঋח NFBO
    TUE
    ਸ ࢎਊೣ

    ޷

    QJYFM@NFBO
    ࡐӝ
    QJYFM@TUE
    ա־ӝ
    QJYFM@NFBO
    ࡐӝ
    88TUE

    ޷

    ೟ण җ੿ 5SBJOJOH5JNF

    ѐࢶ ੹ *OGFSFODF5JNF
    ѐࢶ റ *OGFSFODF5JNF

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  28. ݽ؛ ন੗ച 2VBOUJ[BUJPO
    ੸ਊद੄ ౲

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  29. ݽ؛ ন੗ച 2VBOUJ[BUJPO
    ী ੄ೠ -BUFODZ ѐࢶ
    ֙ ੉റ੄ ٣߄੉झীࢲח JOUҗ G੄ ࢿמ ର੉о ഛোೞ׮
    (16ࠁ׮ب જ׮
    model precision cpu xnnpack gpu nnapi
    Galaxy S7
    f32 32.27 19.50 25.42 32.52
    i8 24.89 24.68 17.38 24.63
    Galaxy S10
    f32 22.36 11.97 12.25 29.41
    i8 14.26 15.27 13.19 16.15
    Galaxy Note20
    f32 12.11 9.31 7.50 14.50
    i8 5.55 5.20 5.81 6.17
    Galaxy S21
    f32 10.36 6.95 5.43 18.51
    i8 4.1 4.4 5.63 8.09
    .PCJMFOFU 7੄ -BUFODZஏ੿ NJMMJ4FDPOET

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  30. ݽ؛ ন੗ച 2VBOUJ[BUJPO
    ী ੄ೠ -BUFODZ ѐࢶ
    ֙ ੉റ੄ ٣߄੉झীࢲח JOUҗ G੄ ࢿמ ର੉о ഛোೞ׮
    (16ࠁ׮ب જ׮
    model precision cpu xnnpack gpu nnapi
    Galaxy S7
    f32 32.27 19.50 25.42 32.52
    i8 24.89 24.68 17.38 24.63
    Galaxy S10
    f32 22.36 11.97 12.25 29.41
    i8 14.26 15.27 13.19 16.15
    Galaxy Note20
    f32 12.11 9.31 7.50 14.50
    i8 5.55 5.20 5.81 6.17
    Galaxy S21
    f32 10.36 6.95 5.43 18.51
    i8 4.1 4.4 5.63 8.09
    .PCJMFOFU 7੄ -BUFODZஏ੿ NJMMJ4FDPOET

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  31. ন੗ച 2VBOUJ[BUJPO
    ী ஘ച੸ ݽ؛ ࢸ҅ о੉٘
    ֎౟ਕ௼੄ %FQUIо ծਸ ࣻ۾ ਬܻ
    8JEUIо ևਸ ࣻ۾ ਬܻ
    -BZFS
    -BZFS
    -BZFS

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  32. ন੗ച 2VBOUJ[BUJPO
    ী ஘ച੸ ݽ؛ ࢸ҅ о੉٘
    ֎౟ਕ௼੄ %FQUIо ծਸ ࣻ۾ ਬܻ
    8JEUIо ևਸ ࣻ۾ ਬܻ
    -BZFS
    -BZFS
    -BZFS

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  33. ন੗ച 2VBOUJ[BUJPO
    ী ஘ച੸ ݽ؛ ࢸ҅ о੉٘
    ਋ഥ҃۽ 4IPSU$POOFDUJPO
    ੉ ੓ਸ ࣻ۾ ਬܻೞ׮
    -BZFS
    -BZFS
    -BZFS




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  34. ۨ੉য ࠙ೡ۽ ন੗ച ࢿמ ೞۅ ѐࢶ
    ୹۱ ౠࢿ੉ ׮ܲ ۨ੉য੄ ޛܻ੸ ࠙ೡ੉ ন੗ചী ਬܻೡ ࣻ ੓׮ 1PTU5SBJOJOH4QMJU

    'VMMZ$POOFDUFE
    weights<128x256>
    bias<128>
    'VMMZ$POOFDUFE
    weights<7x128>
    Bias<7>
    4MJDF
    begin<0>
    end<6>
    4MJDF
    begin<6>
    end<7>
    -PHJTUJD
    $PODBU
    'VMMZ$POOFDUFE
    weights<128x256>
    bias<128>
    'VMMZ$POOFDUFE
    weights<6x128>
    Bias<6>
    -PHJTUJD
    $PODBU
    'VMMZ$POOFDUFE
    weights<1x128>
    Bias<1>
    변경
    -128 127
    -128 127
    -128 127
    f32
    f32 f32

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  35. ۨ੉য ࠙ೡ۽ ন੗ച ࢿמ ೞۅ ѐࢶ
    ୹۱ ౠࢿ੉ ׮ܲ ۨ੉য੄ ޛܻ੸ ࠙ೡ੉ ন੗ചী ਬܻೡ ࣻ ੓׮ 1PTU5SBJOJOH4QMJU

    'VMMZ$POOFDUFE
    weights<128x256>
    bias<128>
    'VMMZ$POOFDUFE
    weights<7x128>
    Bias<7>
    4MJDF
    begin<0>
    end<6>
    4MJDF
    begin<6>
    end<7>
    -PHJTUJD
    $PODBU
    'VMMZ$POOFDUFE
    weights<128x256>
    bias<128>
    'VMMZ$POOFDUFE
    weights<6x128>
    Bias<6>
    -PHJTUJD
    $PODBU
    'VMMZ$POOFDUFE
    weights<1x128>
    Bias<1>
    변경
    -128 127
    -128 127
    -128 127
    f32
    f32 f32

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  36. ۨ੉য ࠙ೡ۽ ন੗ച ࢿמ ೞۅ ѐࢶ
    ୹۱ ౠࢿ੉ ׮ܲ ۨ੉য੄ ޛܻ੸ ࠙ೡ੉ ন੗ചী ਬܻೡ ࣻ ੓׮ 1PTU5SBJOJOH4QMJU

    'VMMZ$POOFDUFE
    weights<128x256>
    bias<128>
    'VMMZ$POOFDUFE
    weights<7x128>
    Bias<7>
    4MJDF
    begin<0>
    end<6>
    4MJDF
    begin<6>
    end<7>
    -PHJTUJD
    $PODBU
    'VMMZ$POOFDUFE
    weights<128x256>
    bias<128>
    'VMMZ$POOFDUFE
    weights<6x128>
    Bias<6>
    -PHJTUJD
    $PODBU
    'VMMZ$POOFDUFE
    weights<1x128>
    Bias<1>
    변경
    -128 127
    -128 127
    -128 127
    f32
    f32 f32

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  37. .PEFM0QUJNJ[BUJPO
    ࣘب৬ ಌನݢझ ࢎ੉੄ ୭੸੼ ଺ӝ

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  38. -PX-BUFODZ )JHI1FSGPSNBODF
    Computation
    Performance

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  39. 0WFSBMM.PEFM0QUJNJ[BUJPO1SPDFTT

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  40. 0WFSBMM1SPDFTT
    4FMFDU5BSHFU%FWJDF 4FBSDI"SDIJUFDUVSF 1FSGPSNBODF0QUJNJ[BUJPO

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  41. 0WFSBMM1SPDFTT
    4FMFDU5BSHFU%FWJDF 4FBSDI"SDIJUFDUVSF 1FSGPSNBODF0QUJNJ[BUJPO

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  42. $PNNPO.PEFM0QUJNJ[BUJPO)FVSJTUJDT

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  43. 4JNQMF$PNQVUBUJPOBM(SBQI 3FEVDF$PNQVUBUJPO 6TF0QUJNJ[FE0QT

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  44. 4JNQMF$PNQVUBUJPOBM(SBQI 3FEVDF$PNQVUBUJPO 6TF0QUJNJ[FE0QT

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  45. %FWJDFBXBSF.PEFM 0QUJNJ[BUJPO)FVSJTUJDT

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  46. $16*OGFSFODF
    10
    15
    20
    25
    30
    120 180 240 300
    Latency(ms)
    FLOPS(M)
    Lower FLOPS, Lower Latency

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  47. (16/16*OGFSFODF
    Lower Complexity, Lower Latency
    Better
    Worse

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  48. %FTJHO4USVDUVSF

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  49. "SDIJUFDUVSF੄ ҳࢿ ਃࣗ

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  50. %FTJHO
    input
    output
    depth
    width
    resolution

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  51. .JDSPBSDIJUFDUVSF

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  52. %FTJHOPG"SDIJUFDUVSF

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  53. 4MJN'BDF %FTJHO
    'BDF3FDPHOJUJPO਷ ҕѐػ QSFUSBJOFECBDLCPOFٜ੄ ೟ण ജ҃җ ௼ѱ ׮ܰ׮
    ୭੸੄ EFTJHOਸ ਤ೧ࢲח → Re-design

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  54. 4MJN'BDF %FTJHO
    most effective receptive field
    most effective receptive field
    Total Capacity
    input output
    1x 2x 4x 8x
    1x 2x 4x 8x
    Receptive Field의 디자인이 전체 디자인의 performance를 결정

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  55. %FTJHO.JDSPBSDIJUFDUVSF

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  56. .JDSPBSDIJUFDUVSF%FTJHO

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  57. 5XP5SBDLT

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  58. SimpleFace
    NAS

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  59. 'PS$16/"4 /FVSBM"SDIJUFDUVSF4FBSDI

    /"4ۆ
    ઱য૓ ܻࣗझ ؘ੉ఠ కझ௼ী ୭੸ചػ
    PQFSBUJPOҗ TUSVDUVSFܳ ঌҊ્ܻਵ۽ ఐ࢝
    DPNQVUBUJPOBMHSBQI੄ п OPEFীࢲ যڃ
    PQFSBUJPOਸ ࢎਊ೧ ׮਺ OPEF۽ ૓೯ೡ૑ Ѿ੿

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  60. 'PS$16/"4 /FVSBM"SDIJUFDUVSF4FBSDI

    1/" 1SPYZMFTT /FVSBM"SDIJUFDUVSF
    "EBQUBUJPO


    TFBSDITQBDFо ևয૑ݶ /"4੄ ബਯ਷ хࣗ
    ӝઓ IVNBOEFTJHOFENPEVMFীࢲ
    DPOOFDUJPO੄ ਬޖ݅ਸ TFBSDIೞৈ ࢿמ ೱ࢚
    1) D.G. Kim and H.C. Lee, "Proxyless Neural Architecture Adaptation at Once," in IEEE Access, 2022

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  61. 'PS$16/"4 /FVSBM"SDIJUFDUVSF4FBSDI

    1/" 1SPYZMFTT /FVSBM"SDIJUFDUVSF
    "EBQUBUJPO


    TFBSDITQBDFо ևয૑ݶ /"4੄ ബਯ਷ хࣗ
    ӝઓ IVNBOEFTJHOFENPEVMFীࢲ
    DPOOFDUJPO੄ ਬޖ݅ਸ TFBSDIೞৈ ࢿמ ೱ࢚
    1) D.G. Kim and H.C. Lee, "Proxyless Neural Architecture Adaptation at Once," in IEEE Access, 2022

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  62. 'PS$16/"4 /FVSBM"SDIJUFDUVSF4FBSDI

    1/" 1SPYZMFTT /FVSBM"EBQUBUJPO

    CBTFMJOF ؀࠺ TLJQDPOOFDUJPOѐ ୶о
    زੌ ো࢑۝ীࢲ ӝઓ 4MJN'BDF ؀࠺ ੋध पಁਯ хࣗ

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  63. 'PS(16/164JNQMF'BDF
    Slower
    Better
    Performance
    Faster
    Moderate
    Performance

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  64. 'PS(16/164JNQMF'BDF
    Training Inference
    Better
    Performance
    Faster

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  65. 'PS(16/164JNQMF'BDF
    4MJN'BDF ؀࠺ زੌ ۨ੉ఢदীࢲ ੋध पಁਯ хࣗ
    4MJN'BDF ؀࠺ زੌ ࢿמীࢲ JOGFSFODFद ࣘب ߓ ૐо

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  66. ,OPXMFEHF%JTUJMMBUJPOUP*NQSPWF1FSGPSNBODF

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  67. ,OPXMFEHF%JTUJMMBUJPO
    Teacher(Pre-trained)
    Student(From scratch)
    Knowledge Distillation
    teacher에서 student로 지식(knowledge)을 증류(distill)하여 전달

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  68. ,OPXMFEHF%JTUJMMBUJPO
    feature extractor(=model) embedding classifier
    logit
    얼굴에 대한 정보를 추상적으로 함축
    학습 데이터의 identity를 확률 분포로 표현
    knowledge to distill
    ঴ҷ ੋध੄ ੹୓ ౵੉೐ۄੋীࢲ ୶୹ೡ LOPXMFEHFܳ Ѿ੿

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  69. ,OPXMFEHF%JTUJMMBUJPO
    hard soft
    student의 학습을 방해하지 않기 위해 smoothing을 통해 soft한 지식을 전달

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  70. ,OPXMFEHF%JTUJMMBUJPO
    UFBDIFS৬ TUVEFOU੄ ର੉о ௼ݶ TUVEFOUо ઁ؀۽ ߓ਋૑ ޅೞӝ ٸޙী TUVEFOUо ୽࠙൤ ழঠ ೞח
    ޙઁ
    ؊ ֫਷ ࢿמਸ о૓ UFBDIFSࠁ׮ زੌೠ ࢿמਸ о૓ UFBDIFSܳ ࢎਊೞח Ѫ੉ ؊ જ਷ ബҗ
    EJTUJMMBUJPOਸ ૓೯ೠ TUVEFOUܳ ੤ର TUVEFOU۽ ॄࢲ ৈ۞ ߣ EJTUJMMBUJPOೡ ҃਋ ࢿמ ೱ࢚
    small gap similar performance distill multiple times

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  71. хࢎ೤פ׮

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