Kaggleで使用される敵対学習方法AWPの論文解説と実装解説 ~Adversarial Weight Perturbation Helps Robust Generalization~

本資料では、AWPという学習手法について、元論文と実装の解説を行っている。

AWPはモデルに敵対的な摂動を加えながら学習するという手法で、汎化能力が高まることからKaggleで人気の手法となっている。

Transcript

1. .BTBLJ"PUB /JLLFJJOD
   ೔ܦ৽ฉཱڭେֶ߹ಉਂ૚ֶशษڧձ!୍ݚڀࣨ
   ,BHHMFͰ࢖༻͞ΕΔ
   

   ఢରֶशํ๏"81ͷ
   

   ࿦จղઆͱ࣮૷ղઆ
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   ؔ࿈ϦϯΫBS9JW/FVS*14(JU)VC,BHHMF൛
   

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2. ࣗݾ঺հ
   ੨ాխً .BTBLJ"PUB
   
   
   w ೔ຊܦࡁ৽ฉࣾ೔ܦΠϊϕʔγϣϯɾϥϘ ݚڀ։ൃ෦ୂ
   
   w ࢴ໘ϏϡʔΞʔͷ0$3Τϯδϯ։ൃ
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   w εΩϧ
   w "U$PEFSਫ৭ ,BHHMF&YQFSU
   w ࢿ֨౳Ԡ༻৘ใɺ฽هڃɺ'1ڃ
   

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3. ໨࣍
   ఢରతࣄྫͷ঺հ ͓࿩͚ͩ
   
   w "EWFSTBSJBM&YBNQMFʹؔ͢Δࣄલ஌ࣝΛಋೖ
   ࿦จղઆ ਺͕ࣜొ৔
   
   w "EWFSTBSJBM8FJHIU1FSUVSCBUJPO
   

   )FMQT3PCVTU(FOFSBMJ[BUJPO
   ࣮૷ղઆ ࣮૷͕ొ৔
   
   w ,BHHMFͰ༻͍ΒΕΔ࣮૷Λ࿦จͷ਺ࣜͱରԠͤͯ͞ղઆ
   

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4. ໨࣍
   ఢରతࣄྫͷ঺հ ͓࿩͚ͩ
   
   w "EWFSTBSJBM&YBNQMFʹؔ͢Δࣄલ஌ࣝΛಋೖ
   ࿦จղઆ ਺͕ࣜొ৔
   
   w "EWFSTBSJBM8FJHIU1FSUVSCBUJPO
   

   )FMQT3PCVTU(FOFSBMJ[BUJPO
   ࣮૷ղઆ ࣮૷͕ొ৔
   
   w ,BHHMFͰ༻͍ΒΕΔ࣮૷Λ࿦จͷ਺ࣜͱରԠͤͯ͞ղઆ
   

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5. ੈքҰ༗໊ͳςφΨβϧ
   ఢରతࣄྫͷྫࣔ
   Ian J. Goodfellow +, Explaining and Harnessing Adversarial Examples, 2014
   ύϯμͷը૾ʹ ਓؒʹ͸Θ͔Βͳ͍ఔ౓ͷ
   

   ઁಈ ϊΠζ
   ΛՃ͑Δͱ
   ςφΨβϧͱ
   ޡೝࣝ͢Δ
   

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6. ఢରతࣄྫ΋ֶशʹՃ͑Δͱ൚Խੑೳ্͕͕Δ
   ఢରతࣄྫ͸ֶशΛؤ݈ʹ͢Δ
   Ian J. Goodfellow +, Explaining and Harnessing Adversarial Examples, 2014
   ྆ํΛQBOEBͱֶͯ͠शͤ͞ΔͱɺఢରతࣄྫҎ֎ʹର͢Δ
   

   ൚Խੑೳ΋޲্͢Δ͜ͱ͕ܦݧతʹ஌ΒΕ͍ͯΔɻ
   111.ίϯϖͰ΋ۚϝμϧΛऔͬͨνʔϜͷ൒෼Ҏ্͸
   

   ఢରతࣄྫΛֶशʹՃ͍͑ͯͨɻ
   

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7. αϯϓϧͷํͰ͸ͳ͘ϞσϧʹޡΒͤΔ͜ͱ΋Մೳ
   Ϟσϧʹ΋ઁಈΛՃ͑Δ
   w
   
   v w + v
   
   ͋ΔϞσϧͷॏΈʹ ͋ΔछͷઁಈΛ
   

   Ճ͑Δͱ
   ѱ͍ϞσϧʹͳΔ
   ֶशதʹϞσϧ͕ѱ͘ͳ͍Α͏ʹઁಈΛՃֶ͑ͭͭश͢Δͱɺ
   ൚Խੑೳ্͕͕Δͱ͍͏࿦จΛ͜Ε͔Βղઆ
   χϡʔϥϧωοτΛલఏʹ͓࿩͠͠·͢
   

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8. ໨࣍
   ఢରతࣄྫͷ঺հ ͓࿩͚ͩ
   
   w "EWFSTBSJBM&YBNQMFʹؔ͢Δࣄલ஌ࣝΛಋೖ
   ࿦จղઆ ਺͕ࣜొ৔
   
   w "EWFSTBSJBM8FJHIU1FSUVSCBUJPO
   

   )FMQT3PCVTU(FOFSBMJ[BUJPO
   ࣮૷ղઆ ࣮૷͕ొ৔
   
   w ,BHHMFͰ༻͍ΒΕΔ࣮૷Λ࿦จͷ਺ࣜͱରԠͤͯ͞ղઆ
   

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9. ࿦จͷ֓ཁ
   ఢରతࣄྫʹର͢Δؤ݈ੑ͕ߴ·ͬͨ
   ໨త ఢରతࣄྫʹର͢Δؤ݈ੑΛߴΊ͍ͨ
   طଘݚڀ ఢରతࣄྫΛ࡞ֶͬͯशʹՃ͑Δख๏͕ଟ͔ͬͨ
   ܦݧత
   

   ࣄ࣮
   ٻ·ͬͨॏΈͷपล͕ΑΓฏΒͳଛࣦʹͳΔ৔߹ɺ
   ఢରతࣄྫʹର͢Δؤ݈ੑ͕ߴ͍
   ख๏ ϞσϧʹఢରతͳઁಈΛՃ͑ͳ͕Βֶश͢Δ
   ݁Ռ
   ՝୊఺ ฏΒͳଛࣦͱͳΔΑ͏ͳ௚઀తͳఆࣜԽ͕ͳ͞Ε͍ͯͳ͍
   

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10. ࿦จͷ֓ཁ
    ໨త ఢରతࣄྫʹର͢Δؤ݈ੑΛߴΊ͍ͨ
    طଘݚڀ ఢରతࣄྫΛ࡞ֶͬͯशʹՃ͑Δख๏͕ଟ͔ͬͨ
    ఢରతࣄྫ΋ֶशʹՃ͑Δ͜ͱͰؤ݈ੑ͕޲্
    

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11. ࿦จͷ֓ཁ
    ఢରతࣄྫʹର͢Δؤ݈ੑ͕ߴ·ͬͨ
    ໨త ఢରతࣄྫʹର͢Δؤ݈ੑΛߴΊ͍ͨ
    طଘݚڀ ఢରతࣄྫΛ࡞ֶͬͯशʹՃ͑Δख๏͕ଟ͔ͬͨ
    ܦݧత
    

    ࣄ࣮
    ٻ·ͬͨॏΈͷपล͕ΑΓฏΒͳଛࣦʹͳΔ৔߹ɺ
    ఢରతࣄྫʹର͢Δؤ݈ੑ͕ߴ͍
    ख๏ ϞσϧʹఢରతͳઁಈΛՃ͑ͳ͕Βֶश͢Δ
    ݁Ռ
    ՝୊఺ ฏΒͳଛࣦͱͳΔΑ͏ͳ௚઀తͳఆࣜԽ͕ͳ͞Ε͍ͯͳ͍
    

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12. ฏΒ͞ͷ֬ೝ
    8FJHIU-PTT-BOETDBQF
    ρ(w) =
    1
    n
    n
    ∑
    i=1
    max
    ∥x′
    
    i
    −xi
    ∥p
    ≤ϵ
    ℓ(fw
    (x′
    
    ), yi
    )
    ఢରతࣄྫΛϞσϧʹೖྗͨ͠৔߹ͷଛࣦ͸ҎԼͷΑ͏ʹఆࣜԽՄೳ
    ఢରతࣄྫΛೖྗ͢Δͱ͍͏ҙຯ
    ρ(w + αd) =
    1
    n
    n
    ∑
    i=1
    max
    ∥x′
    
    i
    −xi
    ∥p
    ≤ϵ
    ℓ(fw+αd
    (x′
    
    ), yi
    )
    ϞσϧͷॏΈͷपลͷଛࣦ͸ҎԼͷΑ͏ʹఆࣜԽՄೳ
    ύϥϝʔλʔXΛֶ࣋ͭशࡁΈϞσϧ
    ௕͞Ћํ޲E͚ͩۙ๣ͷଛࣦ͕ܭࢉͰ͖Δ
    

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13. ฏΒ͞ͷ֬ೝ
    8FJHIU-PTT-BOETDBQF
    Λ ʹ͍ͭͯඳը͢ΔͱɺଛࣦͷฏΒ͞ΛՄࢹԽՄೳ
    Ed
    [ρ(w + αd)] α
    Ed
    [ρ(w + αd)]
    w ॏΈͷपลʹߦ͚͹ߦ͘΄Ͳଛࣦ͕େ͖͘ͳΔ
    w ख๏ʹΑͬͯฏΒ͞͸ҟͳΓͦ͏
    طଘݚڀ
    

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14. ΑΓฏΒͳଛࣦ͸ؤ݈ੑ͕ߴ͍
    طଘݚڀͷ࣮ݧతൺֱ
    w ଛࣦ͕ฏΒͰͳ͍ख๏͸ɺֶशͱςετͷਫ਼౓ͷHBQ͕େ͖͍ "5
    
    w ଛࣦ͕ฏΒʹͳΔख๏͸ɺֶशͱςετͷਫ਼౓ͷHBQ͕খ͍͞ 345 "5&4
    
    w ϞσϧΛؤ݈ʹ͍ͨ͠
    

    ˠॏΈͷۙ๣ͷଛࣦ͕ฏΒͰ͋ΔΑ͏ͳֶशʹ͍ͨ͠
    ֶश࣌ͱςετ࣌ͷੑೳͷΪϟοϓ ଛࣦͷฏΒ͞
    طଘݚڀ
    

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15. ࿦จͷ֓ཁ
    ఢରతࣄྫʹର͢Δؤ݈ੑ͕ߴ·ͬͨ
    ໨త ఢରతࣄྫʹର͢Δؤ݈ੑΛߴΊ͍ͨ
    طଘݚڀ ఢରతࣄྫΛ࡞ֶͬͯशʹՃ͑Δख๏͕ଟ͔ͬͨ
    ܦݧత
    

    

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16. ໰୊ͷఆࣜԽ
    ॏΈͷۙ๣ͷଛࣦ͕ฏΒͰ͋ΔΑ͏ͳֶशʹ͍ͨ͠
    argmin
    w
    max
    v∈
    𝒱
    ρ(w + v)
    ۙ๣ʹ͓͚Δଛࣦͷ࠷େ஋Λ
    ࠷খԽ͢ΔXΛݟ͚ͭΖ
    argmin
    w
    max
    v∈
    𝒱
    1
    n
    n
    ∑
    i=1
    max
    ∥x′
    
    i
    −xi
    ∥p
    ≤ϵ
    ℓ(fw+v
    (x′
    
    ), yi
    )
    ЛΛల։
    ্ه͕ղ͚Ε͹ɺఢରతࣄྫʹؤ݈ͳX͕ಘΒΕΔ͸ͣ
    

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17. ໰୊ͷղ͖ํ
    *OQVU1FSUVSCBUJPO
    argmin
    w
    max
    v∈
    𝒱
    1
    n
    n
    ∑
    i=1
    max
    ∥x′
    
    i
    −xi
    ∥p
    ≤ϵ
    ℓ(fw+v
    (x′
    
    ), yi
    )
    ఢରతࣄྫΛೖྗ͢Δͱ͍͏ҙຯ
    ࠷ॳͷ࠷େԽ໰୊͸طଘݚڀͱಉ͘͡ɺఢରతࣄྫͷೖྗΛҙຯ͢Δ
    ࿦จͰ༻͍͍ͯͨ1(%BUUBDLͷ৔߹ԼهͷY`Λೖྗ͢Ε͹ྑ͍
    x′
    
    i
    ← Πϵ (x′
    
    i
    + η1
    sign(∇x′
    
    i
    ℓ(fw+v
    (x′
    
    i
    ), yi
    ))
    ˞࿦จʹ߹Θͤͨදهʹ͍ͯ͠Δɻ ͸૯৐ͷҙຯͰ͸ͳ͍ɻ
    

    ɹཧղͷͨΊʹແࢹͯ͠΋ྑ͍ɻ
    Π
    

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18. argmin
    w
    max
    v∈
    𝒱
    1
    n
    n
    ∑
    i=1
    max
    ∥x′
    
    i
    −xi
    ∥p
    ≤ϵ
    ℓ(fw+v
    (x′
    
    ), yi
    )
    ໰୊ͷղ͖ํ
    8FJHIU1FSUVSCBUJPO
    ॏΈʹઁಈΛՃ͑ͯ࠷΋ϞσϧΛѱ͍ͨ͘͠
    ͭ໨ͷ࠷େԽ໰୊͸Xۙ๣ͰϞσϧGΛ࠷΋ѱ͘͢Δ͜ͱΛҙਤ
    8VΒ͸ޯ഑Λ༻͍ͯҎԼͷΑ͏ʹWΛࢉग़
    v ← Πγ
    v + η2
    ∇v
    1
    m
    ∑m
    i=1
    ℓ(fw+v
    (x′
    
    i
    ), yi
    )
    ∇v
    1
    m
    ∑m
    i=1
    ℓ(fw+v
    (x′
    
    i
    ), yi
    )
    ∥w∥
    ˞࿦จʹ߹Θͤͨදهʹ͍ͯ͠Δɻ ͸૯৐ͷҙຯͰ͸ͳ͍ɻ
    

    ɹཧղͷͨΊʹແࢹͯ͠΋ྑ͍ɻ
    Π
    

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19. ໰୊ͷղ͖ํ
    8FJHIU1FSUVSCBUJPO
    v ← Πγ
    v + η2
    ∇v
    1
    m
    ∑m
    i=1
    ℓ(fw+v
    (x′
    
    i
    ), yi
    )
    ∇v
    1
    m
    ∑m
    i=1
    ℓ(fw+v
    (x′
    
    i
    ), yi
    )
    ∥w∥
    ˞࿦จʹ߹Θͤͨදهʹ͍ͯ͠Δɻ ͸૯৐ͷҙຯͰ͸ͳ͍ɻ
    

    ɹཧղͷͨΊʹແࢹͯ͠΋ྑ͍ɻ
    Π
    ࠷΋ଛࣦΛ࠷େԽ͢Δ୯ҐϕΫτϧ
    X΁ͷεέʔϧ߹Θͤ
    ֶश཰
    

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20. ໰୊ͷղ͖ํ
    ॏΈͷߋ৽
    argmin
    w
    max
    v∈
    𝒱
    1
    n
    n
    ∑
    i=1
    max
    ∥x′
    
    i
    −xi
    ∥p
    ≤ϵ
    ℓ(fw+v
    (x′
    
    ), yi
    )
    *OQVU1FSUVSCBUJPOͱ8FJHIU1FSUVSCBUJPOΛߦ্ͬͨͰɺ
    ௨ৗͷֶशͱಉ༷ʹόοΫϓϩοϓ͢Ε͹ྑ͍
    ղܾ
    w ← w − η3
    ∇w+v
    1
    m
    m
    ∑
    i=1
    ℓ(fw+v
    (x′
    
    i
    ), yi
    )
    *OQVU1FSUVSCBUJPOͱ8FJHIU1FSUVSCBUJPOΛ
    

    ߦͬͨͱ͖ͷXͷޯ഑
    

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21. ໰୊ͷఆࣜԽ
    ॏΈͷۙ๣ͷଛࣦ͕ฏΒͰ͋ΔΑ͏ͳֶशʹ͍ͨ͠
    argmin
    w
    max
    v∈
    𝒱
    ρ(w + v)
    ۙ๣ʹ͓͚Δଛࣦͷ࠷େ஋Λ
    ࠷খԽ͢ΔXΛݟ͚ͭΖ
    ۩ମతʹ͸
    w ← w − η3
    ∇w+v
    1
    m
    m
    ∑
    i=1
    ℓ(fw+v
    (x′
    
    i
    ), yi
    )
    Ͱύϥϝʔλʔߋ৽Λߦ͑͹͍͍
    

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22. ࣮ݧ݁Ռ
    ˠͨ͠ɻશউɻ
    

    ɹԼهͷදͷ"5"81͕ఏҊख๏ɻ਺ࣈ͕େ͖͍΄Ͳੑೳ͕ྑ͍ɻ
    8FJHIU1FSUVSCBUJPOʹΑͬͯੑೳ޲্͢Δ͔ʁ
    "5ʜ*OQVU1FSUVSCBUJPOͷΈطଘݚڀͷϕʔεϥΠϯ
    "5"81ʜ*OQVU1FSUVSCBUJPO8FJHIU1FSUVSCBUJPO
    

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23. ࣮ݧ݁Ռ
    طଘݚڀ΁ͷ૊ΈࠐΈ$*'"3Λ༻͍࣮ͨݧ
    طଘݚڀͰ͸*OQVU1FSUVSCBUJPOͱଛࣦؔ਺Λॻ͖׵͑Δख๏͕ଟ͍
    8FJHIU1FSUVSCBUJPO͸طଘݚڀʹ΋૊ΈࠐΊΔ
    ֤ྻ͸߈ܸखஈ
    ֤ߦ͸ख๏໊
    "81ͱ͍͍ͭͯΔ΋ͷ͕ఏҊख๏Λ૊ΈೖΕͨ৔߹ͷ݁Ռ
    

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24. ิ଍ࣄ߲
    ,BHHMFͰ͸༻͍ΒΕΔ"81ͷ࣮૷͸ɺ
    *OQVU1FSUVSCBUJPOΛߦΘͣ ఢରతࣄྫ͸࡞Βͣ
    ʹɺ
    8FJHIU1FSUVSCBUJPO͚ͩߦ͏ͷ͕ओྲྀɻ
    Ҏ߱ɺ,BHHMFͰ༻͍ΒΕΔ࣮૷Λલఏʹղઆ͠·͢ɻ
    ࿦จͰ͸ఢରతࣄྫʹର͢Δؤ݈ੑʹ஫໨͍͕ͯͨ͠ɺ
    ܦݧతʹະ஌ͷσʔλʹର͢Δ൚Խೳྗ΋޲্͢Δɻ
    

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25. ໨࣍
    ఢରతࣄྫͷ঺հ ͓࿩͚ͩ
    
    w "EWFSTBSJBM&YBNQMFʹؔ͢Δࣄલ஌ࣝΛಋೖ
    ࿦จղઆ ਺͕ࣜొ৔
    
    w "EWFSTBSJBM8FJHIU1FSUVSCBUJPO
    

    )FMQT3PCVTU(FOFSBMJ[BUJPO
    ࣮૷ղઆ ࣮૷͕ొ৔
    
    w ,BHHMFͰ༻͍ΒΕΔ࣮૷Λ࿦จͷ਺ࣜͱରԠͤͯ͞ղઆ
    ࿦จΦϦδφϧͰ͸ͳ͘ΧϨʔͪΌΜɺ ⊙✱⊙
    ͞ΜͳͲ࣮૷͔Β೿ੜ͍ͯ͠·͢
    

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26. ҰൠతͳֶशMPPQͷྫ
    "81࣮૷ͷલ४උ
    

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27. "81ΛؚΜֶͩशMPPQͷྫ
    "81࣮૷ͷ֓ཁ
    ͜ͷߦΛMPPQதʹૠೖ͢Ε͹ྑ͍
    "81Ϋϥεͷఆٛ͸ͪ͜Β
    

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28. "81ΛؚΜֶͩशMPPQͷྫ
    "81࣮૷ͷ֓ཁͱ਺ࣜͷରԠ
    ߋ৽ଇͷޯ഑෦෼ΛٻΊ͍ͯΔ
    ∇w+v
    1
    m
    m
    ∑
    i=1
    ℓ(fw+v
    (xi
    ), yi
    )
    

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29. "81ΛؚΜֶͩशMPPQͷྫ
    "81࣮૷ͷ֓ཁͱ਺ࣜͷରԠ
    8Λ࣮ࡍʹߋ৽͢Δ෦෼
    w ← w − η3
    ∇w+v
    1
    m
    m
    ∑
    i=1
    ℓ( fw+v
    (xi
    ), yi
    )
    

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30. "81ͷ࣮૷
    ͍ͭ͜Β͸ԿΛ΍͍ͬͯΔͷ͔ʁ
    

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31. "81ͷ࣮૷
    BUUBDL@CBDLXBSEͰ͸ԿΛ΍͍ͬͯΔͷ͔ʁ
    Ϟσϧ Λอଘ
    fw
    ʹվม
    fw+v
    ௚ޙʹCBDLXBSE͍ͯ͠Δͱ͍͏͜ͱ͸ ∇w+v
    1
    m
    m
    ∑
    i=1
    ℓ( fw+v
    (xi
    ), yi
    )
    1
    m
    m
    ∑
    i=1
    ℓ( fw+v
    (xi
    ), yi
    )
    ΛಘΔ͜ͱʹͳΔ
    ˠॏΈXۙ๣Ͱ ४
    ࠷ѱͷଛࣦΛܭࢉ͍ͯ͠Δ
    

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32. "81ͷ࣮૷
    @BUUBDL@TUFQͰ͸ԿΛ΍͍ͬͯΔͷ͔ʁ
    ˠ ʹվม͍ͯ͠Δ
    fw+v
    v ← η2
    ∇v
    1
    m
    ∑m
    i=1
    ℓ( fw+v
    (x′
    
    i
    ), yi
    )
    ∇v
    1
    m
    ∑m
    i=1
    ℓ( fw+v
    (x′
    
    i
    ), yi
    )
    ∥w∥
    

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33. "81ͷ࣮૷
    @BUUBDL@TUFQͰ͸ԿΛ΍͍ͬͯΔͷ͔ʁ
    ˠ ʹվม͍ͯ͠Δ
    fw+v
    
    fw+v
    

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34. "81ͷ࣮૷
    @SFTUPSFͷҙຯ
    Xۙ๣Ͱѱ͍ଛࣦΛܭࢉ
    ͦͷͱ͖ʹϞσϧΛ ʹॻ͖׵͑ͯ͠·ͬͨʂ
    fw+v
    ʹ໭͢ඞཁ͕͋Δ
    fw
    

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35. "81ͷ࣮ફతͳ࢖༻ํ๏
    ࣮ફͰ͸Ϟσϧ͕͋Δఔ౓ֶश͔ͯ͠Βಋೖ͢Δ
    ֶश࣌ؒ΋ϝϞϦ࢖༻ྔ΋ഒఔ౓ͱ͔ͳΓॏ͍
    

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36. "81ͷಋೖྫ
    111.ίϯϖʹ͓͚ΔTDPSFͷਪҠΛՄࢹԽ
    "81ʹΑΔఢରֶशͷ։࢝
    ߴ͍΄Ͳྑ͍
    ίϯϖͰ͸΄Ͳͷ
    ਫ਼౓޲্ʹد༩ͨ͠
    

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37. "EWFSTBSJBM8FJHIU1FSUVSCBUJPO)FMQT3PCVTU(FOFSBMJ[BUJPO
    ·ͱΊ࿦จΛղઆ͠·ͨ͠
    ఢରతࣄྫʹର͢Δؤ݈ੑ͕ߴ·ͬͨ
    ໨త ఢରతࣄྫʹର͢Δؤ݈ੑΛߴΊ͍ͨ
    طଘݚڀ ఢରతࣄྫΛ࡞ֶͬͯशʹՃ͑Δख๏͕ଟ͔ͬͨ
    ܦݧత
    

    ࣄ࣮
    ٻ·ͬͨॏΈͷपล͕ΑΓฏΒͳଛࣦʹͳΔ৔߹ɺ
    ఢରతࣄྫʹର͢Δؤ݈ੑ͕ߴ͍
    ख๏ ϞσϧʹఢରతͳઁಈΛՃ͑ͳ͕Βֶश͢Δ
    ݁Ռ
    ՝୊఺ ฏΒͳଛࣦͱͳΔΑ͏ͳ௚઀తͳఆࣜԽ͕ͳ͞Ε͍ͯͳ͍
    

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38. -PPQͷதʹߦ௥Ճ͢Δ͚ͩͰ࣮૷Մೳ
    ·ͱΊ࣮૷Λղઆ͠·ͨ͠
    ͜ͷߦΛMPPQதʹૠೖ͢Ε͹ྑ͍
    "81Ϋϥεͷఆٛ͸ͪ͜Β
    

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