‫׌‬༠᧑੻‫࡝߂ݬ‬ᏛឹǶፀ))ॎፀ⏕‫'⏨ش‬$ ⑰ '5⏌'"'$

ࠑሗ࿌ࠒ

CycleGAN ࢆ⏝࠸ࡓᆅᇦ≉ᛶࢆᣢࡘࡈᙜᆅ࢟ࣕࣛࡢ⏕ᡂ㛗ᑿගᝋ  ⠈ᑦె ͊Creation of a local mascot with regional characteristics using CycleGAN
Mitsuyoshi NAGAO*  Hisayoshi TAKAMURA†

せ᪨㏆ᖺ㸪඲ᅜⓗ࡟ࠕࡺࡿ࢟ࣕࣛࠖࢆ⏝࠸࡚ PR άືࢆ⾜࠺ᆅ᪉⮬἞యࡀቑຍࡋ࡚࠸ࡿࠋ≉࡟㸪ᆅᇦࡢ PR ࢆ┠ⓗ࡜ࡋࡓࡺࡿ࢟ࣕࣛࡣ㸪ࡈᙜᆅ࢟ࣕࣛ࡜ࡶ࿧ࡤࢀ࡚࠸ࡿࠋ⌧ᅾ㸪ᩘከࡃࡢᆅ᪉⮬἞యࡀࡈᙜᆅ࢟ࣕࣛࢆ᭷ࡋ࡚࠾ࡾ㸪᭦࡟㸪ᩘከࡃࡢࡺࡿ࢟ࣕࣛࡶไసࡉࢀ࡚࠸
ࡿࡓࡵ㸪᪂ࡓ࡞ࡈᙜᆅ࢟ࣕࣛࢆసᡂࡍࡿປຊࡣ኱ࡁ࠸ࡶࡢ࡜࡞ࡗ࡚࠸ࡿࠋᮏㄽ࡛ࡣ㸪
CycleGAN ࢆ⏝࠸ࡓᆅᇦ≉ᛶࢆᣢࡘࡈᙜᆅ࢟ࣕࣛࡢ⏕ᡂࢆᥦ᱌ࡍࡿࠋGAN ࡣ㸪ࢹ࢕࣮ࣉࣛ
࣮ࢽࣥࢢࡢᡭἲࢆ⏝࠸ࡓ⏬ീ⏕ᡂ᪉ἲࡢ୍ࡘ࡛࠶ࡾ㸪୰࡛ࡶ CycleGAN ࡣ஧✀㢮ࡢ⏬ീ㛫ࡢࢫࢱ࢖ࣝࢆኚ᥮ࡍࡿࡇ࡜ࡀྍ⬟࡞ᡭἲ࡛࠶ࡿࠋࡇࡇ࡛ࡣ㸪໭ᾏ㐨኱✵⏫ࢆࡈᙜᆅ࢟ࣕࣛ
⏕ᡂࡢࡓࡵࡢᑐ㇟ᆅᇦ࡜ࡋ࡚᥇⏝ࡋ㸪኱✵⏫ࡢ≉⏘ရ࡜ࡺࡿ࢟ࣕࣛࡢࢫࢱ࢖ࣝࢆኚ᥮ࡍࡿ
ࡇ࡜࡟ࡼࡾࡈᙜᆅ࢟ࣕࣛࢆ⏕ᡂࡍࡿࠋ

Abstract
In Japan, local governments conduct public relations using mascots. These are called local mascots.
Currently, the creation of any new local mascot requires money and effort. Many local governments
have unofficial local mascots in addition to official ones. This paper outlines the creation of a local
mascot with regional characteristics using CycleGAN. GAN is a way to create images based on thedeep learning method. CycleGAN can convert the style between two different kinds of image. We
explain how we created a mascot for the town of Ozora, Hokkaido, using its regional characteristics
by combining the image styles of various existing mascot with images of the town of Ozora.

࣮࣮࢟࣡ࢻᆅᇦ≉ᛶ㸦regional characteristics㸧 ࡈᙜᆅ࢟ࣕࣛ㸦local mascot㸧*†໭ᾏ㐨᝟ሗ኱Ꮫ⤒Ⴀ᝟ሗᏛ㒊ࢩࢫࢸ࣒᝟ሗᏛ⛉ᩍᤵ, Professor, Department of Systems and Informatics, HIU໭ᾏ㐨᝟ሗ኱Ꮫ⤒Ⴀ᝟ሗᏛ㒊ࢩࢫࢸ࣒᝟ሗᏛ⛉ B4, B4, Department of Systems and Informatics, HIU

㸫1㸫⏎%⏎ᬏ࣊Ƕӵ੯ͺ፳ǶǶࢼч࡞ປຊࡀᚲせ࡜ࡉࢀࡿࠋ㸬 ࡣࡌࡵ࡟୍᪉㸪㏆ᖺ㸪ᶵᲔᏛ⩦ࡢ୍ࡘࡢᡭἲ࡛࠶ࡿࢹ࢕࣮ࣉ࣮ࣛࢽࣥࢢ࡟ὀ┠ࡀ㞟ࡲࡗ࡚࠸ࡿࠋ

㏆ᖺ㸪඲ᅜⓗ࡟ࠕࡺࡿ࢟ࣕࣛࠖࢆ⏝࠸࡚ࢹ࢕࣮ࣉ࣮ࣛࢽࣥࢢ࡜ࡣ㸪ே㛫ࡢ⬻⚄⤒⣽⬊PR άືࢆ⾜࠺ᆅ᪉⮬἞యࡀቑຍࡋ࡚࠸ࡿࠋࡢ௙⤌ࡳࢆᶍೌࡋࡓ㸪ࢽ࣮ࣗࣛࣝࢿࢵࢺ࣮࣡ࡺࡿ࢟ࣕࣛ࡜ࡣࠕࡺࡿ࠸࣐ࢫࢥࢵࢺ࢟ࣕࣛࢡࢡࢆᇶ┙࡜ࡋ࡚࠸ࡿᢏ⾡࡛࠶ࡾ㸪⏬ീㄆ㆑࡞ࢱ࣮ࠖࡢ␎࠶ࡾ㸪࡯ࡢࡰࡢ࡜ࡋࡓ⊂≉ࡢ㞺ᅖ࡝ࡢศ㔝࡟࠾࠸࡚ά⏝ࡀ㐍ࢇ࡛࠸ࡿࠋ᭦࡟㸪Ẽࢆᣢࡘ࣐ࢫࢥࢵࢺ࢟ࣕࣛࢡࢱ࣮ࡢ⥲⛠࡛࠶⌧ᅾ㸪ࡇࡢࢹ࢕࣮ࣉ࣮ࣛࢽࣥࢢࢆ฼⏝ࡋࡓ⏬

ࡿࠋ≉࡟㸪ᆅᇦࡢ PR ࢆ┠ⓗ࡜ࡋࡓࡶࡢࡣ㸪ീ⏕ᡂ᪉ἲ࡟㛵ࡍࡿ◊✲ࡀ⾜ࢃࢀ࠾ࡾ㸪୰࡛ࡈᙜᆅ࢟ࣕࣛ࡜ࡶ࿧ࡤࢀ࡚࠸ࡿࠋ

ࡶ㸪2014 ᖺ࡟ Goodfellow ࡽࡀᥦ᱌ࡋࡓ㸪ࡺࡿ࢟ࣕࣛࡢ⤒῭ຠᯝࡣ㠀ᖖ࡟኱ࡁ࡞ࡶࡢᩛᑐⓗ⏕ᡂࢿࢵࢺ࣮࣡ࢡ (GAN㸸Generative࡟࡞ࡗ࡚࠸ࡿࠋ౛࠼ࡤ㸪⇃ᮏ┴ࡢࡺࡿ࢟ࣕࣛAdversarial Network) ࢆ⏝࠸ࡓ⏬ീ⏕ᡂࡀὀ࡛࠶ࡿࠕࡃࡲࣔࣥࠖࡀ኱ࡁ࡞ᡂຌ౛࡜ࡋ࡚ᣲ┠ࢆ㞟ࡵ࡚࠸ࡿ㸦Goodfellow et al., 2014㸧ࠋࡆࡽࢀࡿࠋࡃࡲࣔࣥࡣ 2010 ᖺ 3 ᭶࡟⇃ᮏ┴

GAN ࡣ㸪Ꮫ⩦ࢹ࣮ࢱࡢ⏬ീ࡜ぢศࡅࡀࡘ࠿ࡀⓎ⾲ࡋࡓ࣐ࢫࢥࢵࢺ࢟ࣕࣛࢡࢱ࣮࡛࠶ࡾ㸪࡞࠸᪂ࡓ࡞⏬ീࢆ⏕ᡂࡍࡿ⏕ᡂჾ࡜㸪⏕ᡂჾࡑࡢ⤒῭ຠᯝࡣ㸪 2011 ᖺ 11 ᭶࠿ࡽ 2013 ᖺࡀ⏕ᡂࡋࡓ⏬ീ࡜Ꮫ⩦ࢹ࣮ࢱ⏬ീࢆ㆑ูࡍࡿ

10 ᭶ࡢ 2 ᖺ㛫࡛㸪ࡃࡲࣔࣥ฼⏝ၟရࡢ኎ୖ㆑ูჾࡀ஫࠸࡟Ꮫ⩦ࡋྜ࠺ࡇ࡜࡟ࡼࡾ㸪Ꮫ⩦ࡆ㸪ཬࡧ㸪ほගᐈቑຍ࡟ࡼࡿ⤒῭Ἴཬຠᯝࡀࢹ࣮ࢱ⏬ീ࡜ぢศࡅࡀࡘ࠿࡞࠸㧗⢭ᗘ࡞⏬ീ

1244 ൨෇㸪ࢸࣞࣅࡸ᪂⪺࡟ྲྀࡾୖࡆࡽࢀࡓࢆ⏕ᡂࡍࡿࡇ࡜ࡀྍ⬟࡞ᡭἲ࡛࠶ࡿࠋ

ࡓࡇ࡜࡟ࡼࡿࣃࣈࣜࢩࢸ࢕ຠᯝࡣ 90 ൨෇㸪ࡇࢀࡲ࡛࡟ GAN ࢆ⏝࠸ࡓ࢟ࣕࣛࢡࢱ࣮⏕᭦࡟㸪ほගᐈࡢቑຍᩘࡣ 18.8 ୓ே࡜ሗ࿌ࡉ

ᡂࡢヨࡳࡶ⾜ࢃࢀ࡚࠸ࡿࠋᰩཎࡽࡣ㸪ₔ⏬ᐙࢀ࡚࠸ࡿ㸦᪥ᮏ㖟⾜⇃ᮏᨭᗑ, 2013㸧ࠋ࡛࠶ࡿᡭሯ἞⹸ࡢₔ⏬ࡢ෌⌧ࢆ┠ᶆ࡟㸪ᡭሯࡲࡓ㸪඲ᅜⓗ࡞ࡺࡿ࢟ࣕࣛேẼࡢᗈࡀࡾ࡟

Ặ࡟ࡼࡗ࡚ᥥ࠿ࢀࡓ࢟ࣕࣛࢡࢱ࣮ࢆ GAN ࡟ࡼࡾ㸪2011 ᖺ࠿ࡽ 2020 ᖺ࡟㸪ࡺࡿ࢟ࣕࣛࢢ

ࡼࡗ࡚Ꮫ⩦ࡋ㸪᪂ࡓ࡞࢟ࣕࣛࢡࢱ࣮⏬ീࢆ⏕ࣛࣥࣉࣜ࡜࿧ࡤࢀࡿࡺࡿ࢟ࣕࣛࡢேẼᢞ⚊ࢆᡂࡋ㸪ࡇࢀࢆᇶ࡟ேᡭ࡟ࡼࡾ᪂ࡓ࡞ₔ⏬ࡢ࢟⾜࠺࢖࣋ࣥࢺࡶ㛤ദࡉࢀ࡚ࡁࡓࠋࡇࡢ࢖࣋ࣥࣕࣛࢡࢱ࣮ࢆసᡂࡋ࡚࠸ࡿ㸦ᰩཎࡽ㸪ࢺࡢேẼࡣ㧗ࡃ㸪2015 ᖺࡢ኱఍࡛ࡣ㸪1727ࠋࡇࡢࡼ࠺࡟㸪GAN ࢆ⏝࠸ࡿࡇ࡜࡛᪂
2020㸧యࡶࡢ࢟ࣕࣛࢡࢱ࣮ࡀཧຍࡋ࡚࠸ࡿࠋࡓ࡞࢟ࣕࣛࢡࢱ࣮ࢆసᡂࡍࡿ㝿ࡢ࣮࣋ࢫ࡜࡞ࡇࡢࡼ࠺࡟㸪ࡺࡿ࢟ࣕࣛࡣᆅᇦࡢ PR ࡸᆅࡿ⏬ീࢆ⏕ᡂࡍࡿࡇ࡜ࡀ࡛ࡁࡿࡇ࡜ࡀሗ࿌ࡉᇦ⤒῭࡬ࡢᙳ㡪࡜࠸࠺ほⅬ࡟࠾࠸࡚᭷ຠᛶࡢࢀ࡚࠸ࡿࠋࡑࡇ࡛㸪ᮏ◊✲࡛ࡣ㸪GAN ࢆ⏝㧗࠸ࡶࡢ࡛࠶ࡿࠋࡋ࠿ࡋ࡞ࡀࡽ㸪ࡺࡿ࢟ࣕࣛ࠸ࡿࡇ࡜࡟ࡼࡾ㸪ᆅᇦ≉ᛶࢆ᭷ࡍࡿ᪂ࡓ࡞ࡈࢢࣛࣥࣉࣜࡢཧຍ࢟ࣕࣛࢡࢱ࣮ᩘ࠿ࡽࡶࢃ࠿ᙜᆅ࢟ࣕࣛࢆసᡂࡍࡿࡓࡵࡢᨭ᥼ࡀ࡛ࡁࡿࡢࡿࡼ࠺࡟㸪⌧ᅾ㸪ከ✀ከᵝ࡞ࡺࡿ࢟ࣕࣛࡀస࡛ࡣ࡞࠸࠿࡜⪃࠼ࡓࠋᡂࡉࢀ࡚࠸ࡿࡓࡵ㸪᪂ࡓ࡞࢟ࣕࣛࢡࢱ࣮ࢆసᮏㄽ࡛ࡣ㸪CycleGAN ࢆ⏝࠸ࡓᆅᇦ≉ᛶࢆࡿࡇ࡜ࡣᅔ㞴࡟࡞ࡗ࡚ࡁ࡚࠸ࡿࠋຍ࠼࡚㸪ᆅᣢࡘࡈᙜᆅ࢟ࣕࣛࡢ⏕ᡂࢆᥦ᱌ࡍࡿࠋCycle

ᇦࡢ PR ࢆ⾜࠺ࡈᙜᆅ࢟ࣕࣛ࡟࠾࠸࡚ࡣ㸪࢟GAN ࡣ㸪㸰✀㢮ࡢ⏬ീ㛫ࡢࢫࢱ࢖ࣝࢆኚ᥮ࣕࣛࢡࢱ࣮ࡢ≉ᚩ࡜ࡋ࡚㸪ᆅᇦࡢ≉⏘ရࡸほࡍࡿࡇ࡜ࡀྍ⬟࡞ GAN ࡢ୍ᡭἲ࡛࠶ࡿࠋࡇගྡᡤ࡞࡝ࡢᆅᇦ≉ᛶࢆྲྀࡾධࢀࡿᚲせࡀ࠶ࡇ࡛ࡣ㸪Ꮫ⩦ࢹ࣮ࢱ࡜ࡋ࡚㸪ࡺࡿ࢟ࣕࣛࢢࣛࡿࡓࡵ㸪᪂ࡓ࡞࢟ࣕࣛࢡࢱ࣮ࡢసᡂ࡟ࡣ኱ࡁࣥࣉࣜ࡟ฟሙࡋࡓ࢟ࣕࣛࢡࢱ࣮㸪ཬࡧ㸪໭ᾏ㸫2㸫⏎&⏎I´PE^ ˂ᅎȥɜ‫ݖܩ‬႕ਸ˂୫ɥɆয়‫͒͝˦ܩ‬ɺᅊଁ㐨኱✵⏫ࡢ≉⏘ရࡢ⏬ീࢹ࣮ࢱࢆᏛ⩦ࢹ࣮ࢱ

GAN ࡜ ࡋ ࡚ ὀ┠ ࡉ ࢀ࡚ ࠸ ࡿ 㸦 Zhu et al.,࡜ࡋ࡚⏝࠸㸪ࡇࢀࡽ⏬ീ㛫࡟࠾࠸࡚ࢫࢱ࢖ࣝ

2017㸧
ࠋኚ᥮ࢆ⾜࠺ࡇ࡜࡟ࡼࡾ᪂ࡓ࡞⏬ീࢆ⏕ᡂࡍ

ᅗ 2 ࡟ CycleGAN ࡟࠾ࡅࡿᏛ⩦ࣉࣟࢭࢫࢆࡿࠋࡇࢀ࡟ࡼࡾᆅᇦ≉ᛶࢆᣢࡘࡈᙜᆅ࢟ࣕࣛ♧ࡍࠋᅗ࡛ࡣ㸪ࢫࢱ࢖ࣝኚ᥮ࡍࡿ 2 ✀㢮ࡢ⏬

ࡢ⏬ീࢆ⏕ᡂࡍࡿࠋീࢆ⏬ീ X㸪⏬ീ Y㸪ࢫࢱ࢖ࣝኚ᥮ࡉࢀࡓ⏬
ീࢆ X’㸪Y’࡜ࡋ࡚࠸ࡿࠋ⏕ᡂჾ࡜㆑ูჾࡣ㸰✀㢮࠶ࡾ㸪X ࠿ࡽ Y ࡬ࡢࢫࢱ࢖ࣝኚ᥮ࢆ⾜࠺㸬 GAN ࡜ CycleGAN⏕ᡂჾࢆ F㸪Y ࠿ࡽ X ࡬ࡢࢫࢱ࢖ࣝኚ᥮ࢆ⾜࠺⏕ᡂჾࢆ G ࡜ࡋ࡚࠸ࡿࠋ㆑ูჾ DX ࡣ X ࡀᏛ⩦⏝ࡢ⏬ീ࡛࠶ࡿ࠿㸪⏕ᡂჾࡀ⏕ᡂࡋࡓኚ GANGAN ࡣ㸪Goodfellow ࡽ࡟ࡼࡗ࡚ᥦ᱌ࡉ᥮⏬ീ࡛࠶ࡿ࠿ࢆ㆑ูࡍࡿࠋ㆑ูჾ DY ࡣྠᵝࢀࡓᶵᲔᏛ⩦࡟ࡼࡿ⮬ື⏬ീ⏕ᡂࡢᡭἲ࡛࡟⏬ീ Y ࡟ࡘ࠸࡚ࡢ㆑ูࢆ⾜࠺ࠋ⏬ീ X㸪Y࠶ࡾ㸪ᩍᖌ࡞ࡋᏛ⩦ࡢ⏬ീ⏕ᡂࣔࢹࣝࡢ୍ࡣࡑࢀࡒࢀࡢ㆑ูჾ࡜⏕ᡂჾ࡟ධຊࡉࢀࡿࠋ✀࡛࠶ࡿࠋGAN ࡟࠾࠸࡚ࡣ㸪⏬ീࢆ⏕ᡂ⏕ᡂჾࡣධຊࡉࢀࡓ⏬ീࢆඖ࡟ኚ᥮ඛࡢ⏬ീࡍࡿ⏕ᡂჾ࡜⏕ᡂࡉࢀࡓ⏬ീ࠿࡝࠺࠿㆑ู࡟ኚ᥮ࢆ⾜࠺ࠋኚ᥮ࡉࢀࡓ⏬ീࡢ X’㸪Y’ࡣ㆑ࡍࡿ㆑ูჾࡀ஫࠸࡟Ꮫ⩦ࡋྜ࠺ࡇ࡜࡟ࡼ

ูჾ DY㸪DX ࡛㆑ูࡀ⾜ࢃࢀࡿࠋࡾ㸪Ꮫ⩦⏝ࡢ⏬ീ࡜ぢศࡅࡀࡘ࠿࡞࠸⏬ീࡇࢀࡽ㆑ูჾ㸪ཬࡧ㸪⏕ᡂჾ࡟࠾࠸࡚㸪ᩛᑐࢆ⏕ᡂࡍࡿࡇ࡜ࡀ࡛ࡁࡿࠋGAN ࡟ࡼࡿ⏬

ᛶᦆኻ㸪ࢧ࢖ࢡ୍ࣝ㈏ᛶᦆኻ㸪ྠ୍ᛶᦆኻࢆ฼ീ⏕ᡂࡢࣉࣟࢭࢫࢆᅗ㸯࡟♧ࡍࠋ⏝ࡍࡿࡇ࡜࡟ࡼࡾᏛ⩦ࡀ⾜ࢃࢀࡿࠋ㆑ูჾ࡛ᅗ 1 ࡟♧ࡉࢀࡿࡼ࠺࡟㸪ࣛࣥࢲ࣒ࣀ࢖ࢬࡣ㸪ᩛᑐᛶᦆኻ㸪⏕ᡂჾ࡛ࡣ㸪3 ࡘࡢᦆኻࢆ฼࡜࿧ࡤࢀࡿ஘ᩘࢆᇶ࡟⏕ᡂჾࡣ⏬ീࢆ⏕ᡂ⏝ࡋ࡚Ꮫ⩦ࡀ⾜ࢃࢀࡿࠋᩛᑐᛶᦆኻ࡜ࡣ㸪୍⯡ࡍࡿࠋᏛ⩦ึᮇࡣ㆑ูჾ࡟ࡼࡾ㸪ഇ≀࡜㆑ⓗ࡞ GAN ࡢᦆኻ㛵ᩘ࡜ྠᵝ࡛࠶ࡾ㸪⏕ᡂჾูࡉࢀࡿ⏬ീࡀ⏕ᡂࡉࢀࡿࡀ㸪⏕ᡂჾࡢᏛ࡟ࡼࡿഇ⏬ീ࡛࠶ࡿ࠿Ꮫ⩦ࢹ࣮ࢱ⏬ീ࡛࠶ࡿ⩦ࡀ㐍ࡴ࡟ࡘࢀ㸪㆑ูᶵ࡟ࡼࡾᏛ⩦ࢹ࣮ࢱ࠿ࢆ㆑ูჾ࡟ࡼࡗุ࡚ᐃࡍࡿࡇ࡜࡟ࡼࡾィ⟬࡜㆑ูࡉࢀࡿഇࡢ⏬ീࢆ⏕ᡂࡍࡿࡇ࡜ࡀ࡛ࡉࢀࡿᦆኻ࡛࠶ࡿࠋࢧ࢖ࢡ୍ࣝ㈏ᛶᦆኻ࡜ࡣ㸪ࡁࡿࡼ࠺࡟࡞ࡿࠋࡇࡢࡼ࠺࡟㸪GAN ࡟࠾ኚ᥮ࡉࢀࡓ⏬ീࢆ෌ኚ᥮ࡋࡓ⏬ീࡀ㸪ඖࡢ⏬

࠸࡚ࡣ㸪⏬ീ࠿ࡽ≉ᚩࢆᏛ⩦ࡍࡿࡇ࡜࡛ᐇീ࡜୍⮴ࡍࡿࡼ࠺ィ⟬ࡉࢀࡿᦆኻ࡛࠶ࡿࠋࢫᅾࡋ࡞࠸⏬ീࢆ⏕ᡂࡍࡿࡇ࡜ࢆྍ⬟࡜ࡋ࡚ࢱ࢖ࣝኚ᥮ࢆ⾜࠺⏕ᡂჾࡣ㸪ධຊ⏬ീࡢ࠺ࡕ࠸ࡿࠋᚲせ⟠ᡤࡢࡳࢆኚ᥮ࡍࡿࡼ࠺࡟カ⦎ࡉࢀࡿࡇ࡜ࡀᮃࡲࢀࡿࠋࡇࡢࡓࡵ㸪ྠ୍ᛶᦆኻࡣ㸪୙せ࡞⏬ീኚ᥮࡟࣌ࢼࣝࢸ࢕ࢆ୚࠼ࡿࡓࡵࡢᦆኻ࡛࠶ࡾ㸪㹗࠿ࡽ㹖࡟ኚ᥮ࢆ⾜࠺⏕ᡂჾ㹅࡟㹖 CycleGAN⌧ᅾ㸪GAN ࡟࠾࠸࡚ࡣ㸪␚ࡳ㎸ࡳᏛ⩦ࢆ⾜ࢆධຊࡋ㹖Ѝ㹖'࡟ኚ᥮㸪㹖࠿ࡽ㹗࡟ኚ᥮ࡍࡿ

࠺ࡓࡵࡢ DCGAN㸦Deep Convolutional GAN㸧⏕ᡂჾ F ࡟ Y ࢆධຊࡋ YЍY'࡜ኚ᥮ࡋࡓሙྜ㸪ࡸ ᮲ ௳ ࢆ ࡘ ࡅ ⏬ ീ ࢆ ⏕ ᡂ ࡍ ࡿ CGANࡑࢀࡒࢀ୍⮴ࡍࡿࡼ࠺ィ⟬ࡉࢀࡿᦆኻ࡛࠶ࡿࠋ

㸦Conditional GAN㸧࡞࡝ࡀᥦ᱌ࡉࢀ࡚࠸ࡿ

ᅗ 3 ࡟ Zhu ࡽ ࡟ ࡼ ࡗ ࡚ ᐇ ᪋ ࡉ ࢀ ࡓ㸦Radford et al.㸪2015㸧㸦Mirza et al.,2016㸧ࠋCycleGAN ࢆ฼⏝ࡋࡓ⏬ീኚ᥮ࡢ౛ࢆ♧ࡍࠋࡑࡢ୰࡛ࡶ CycleGAN ࡣ㸪2 ✀㢮ࡢ␗࡞ࡿ⏬

ᅗ 3 ࡛ࡣ㸪㸰ࡘࡢ⏬ീࡢࢫࢱ࢖ࣝࢆ┦஫࡟ኚീ㛫ࡢࢫࢱ࢖ࣝࢆ┦஫࡟ኚ᥮ࡍࡿ஦ࡀ࡛ࡁࡿ᥮ࡋ࡚࠾ࡾ㸪ୖẁᕥࡣ㸪ࣔࢿࡢ⤮⏬ࢆ෗┿ࡢࡼ㸫3㸫⏎(⏎ᬏ࣊Ƕӵ੯ͺ፳ǶǶࢼчᮏ≀ࡢ㸦Ꮫ⩦ࣛࣥࢲ࣒ࣀ࢖ࢬ㆑ูჾ┿㸦Ꮫ⩦ࢹ࣮ࢱ㸧RUഇ㸦⏕ᡂࡋࡓ⏬ീ㸧ഇࡢࢹ࣮ࢱ㸦⏕ᡂࡋࡓ⏬ീ㸧⏕ᡂჾᅗ  *$1 ࡟ࡼࡿ⏬ീ⏕ᡂࣉࣟࢭࢫ㆑ูჾ⏬ീ⏕ᡂჾኚ᥮⏬ീ┿㸦Ꮫ⩦ࢹ࣮ࢱ㸧
RUഇ㸦⏕ᡂࡋࡓ⏬ീ㸧㻔䝗䝯䜲䞁㼅䛾⏬ീ㻕෌ኚ᥮⏬ീ㆑ูჾ⏕ᡂჾ┿㸦Ꮫ⩦ࢹ࣮ࢱ㸧RU
ഇ㸦⏕ᡂࡋࡓ⏬ീ㸧㆑ูჾ⏬ീ⏕ᡂჾኚ᥮⏬ീ┿㸦Ꮫ⩦ࢹ࣮ࢱ㸧
RU
ഇ㸦⏕ᡂࡋࡓ⏬ീ㸧㻔䝗䝯䜲䞁㼄䛾⏬ീ㻕

෌ኚ᥮⏬ീ

㆑ูჾ

⏕ᡂჾ┿㸦Ꮫ⩦ࢹ࣮ࢱ㸧
RU
ഇ㸦⏕ᡂࡋࡓ⏬ീ㸧ᅗ  &\FOH*$1 ࡟࠾ࡅࡿ⏬ീ⏕ᡂࣉࣟࢭࢫ

ᅗ  &\FOH*$1 ࡟࠾ࡅࡿ⏬ീኚ᥮౛㸦=KXHWDO㸧

㸫4㸫⏎*⏎I´PE^ ˂ᅎȥɜ‫ݖܩ‬႕ਸ˂୫ɥɆয়‫͒͝˦ܩ‬ɺᅊଁࡈᙜᆅ࢟ࣕࣛࡢ⏕ᡂ࠺࡟㸪෗┿ࢆࣔࢿࡢ⤮⏬ࡢࡼ࠺࡟ኚ᥮ࡋ࡚࠸ࡿࠋୖẁᕥࡣ㸪ࢩ࣐࣐࢘ࢆ࣐࢘࡟ኚ᥮㸪࣐࢘ࢆ ᴫせࢩ࣐࣐࢘࡟ኚ᥮㸪ୗẁࡣ㸪ኟࡢ㢼ᬒ෗┿ࢆ෤࡟㸪෤ࡢ㢼ᬒ෗┿ࢆኟ࡟ኚ᥮ࡋ࡚࠸ࡿ౛࡛࠶ᮏ◊✲࡛ࡣ㸪CycleGAN ࢆ⏝࠸࡚ᆅᇦࡢ≉ࡿࠋ⏘ရࡸほගྡᡤ࡞࡝ࡢᆅᇦᛶࢆᣢࡘࡈᙜᆅ࢟

 CycleGAN ࢆ⏝࠸ࡓ◊✲஦౛࡜ࡋ࡚㸪༓ⴥࣕࣛࡢ⏕ᡂࢆ⾜࠺ࠋලయⓗ࡟ࡣ㸪ࡺࡿ࢟ࣕࣛࡢ

ࡽ㸦2019㸧ࡣ㸪㣗࡭࠿ࡅࡢ㣗࡭≀ࡢ⏬ീࢆ㣗⏬ീࢆ≉⏘ရ➼࡬ኚ᥮㸪ྠ᫬࡟ᆅ᪉ࡢ≉⏘≀࡭ࡿ๓ࡢ≧ែࡢ⏬ീ࡬ኚ᥮ࢆ⾜ࡗ࡚࠸ࡿࠋࡇ➼ࢆࡺࡿ࢟ࣕࣛ࡬ኚ᥮ࡍࡿࠋᮏ◊✲࡛ࡣ㸪ࡈᙜࡇ࡛ࡣ㸪17 ✀㢮ࡢ㣗࡭≀࡟࠾࠸࡚ኚ᥮ࢆᐇᆅ࢟ࣕࣛࢆ⏕ᡂࡍࡿᆅᇦ࡜ࡋ࡚㸪໭ᾏ㐨኱✵᪋ࡋ࡚࠾ࡾ㸪㣗࡭ࡓᚋࡢෆ㒊࡜እほࡢⰍࡀ␗⏫ࢆ᥇⏝ࡍࡿࠋ໭ᾏ㐨኱✵⏫ࡣ㸪໭ᾏ㐨ࡢ࢜࣍࡞ࡿ㣗࡭≀࡟㛵ࡋ࡚ࡣⰋዲ࡞ኚ᥮ࡀᐇ᪋࡛ࡁ࣮ࢶࢡᾏഃ࡟఩⨨ࡍࡿ⏫࡛࠶ࡾ㸪2006 ᖺ࡟ዪࡿࡇ࡜ࡀሗ࿌ࡉࢀ࡚࠸ࡿࠋ‶ู⏫࡜ᮾ⸴⍆ᮧࡀྜేࡋ㸪኱✵⏫࡜࿨ྡࡉࡲࡓ㸪㛗ᕊ㸦2020㸧ࡣ㸪࢟ࣕࣛࢡࢱ࣮ࢹࢨࢀࡓᆅᇦ࡛࠶ࡿࠋ⏫ྡࡢ⏤᮶ࡣ㸪ዪ‶ู✵ ࡀ࢖ࢼ࣮ࢆᨭ᥼ࡍࡿࡓࡵ㸪CycleGAN ࢆ⏝࠸࡚࠶ࡿࡇ࡜࡟㉳ᅉࡍࡿࠋࡲࡓ㸪≉⏘ရ࡜ࡋ࡚⥙㉮▼࡜ዿ᛹ࡢ⏬ീࡢࢫࢱ࢖ࣝኚ᥮ࢆ⾜࠸㸪᪂ࡓ†࡛᥇ࡽࢀ࡚࠸ࡿࢩࢪ࣑ࡀ᭷ྡ࡛࠶ࡿࠋ኱✵࡞ዿ᛹⏬ീࡢ⏕ᡂࢆ⾜ࡗ࡚࠸ࡿࠋࡇࡇ࡛ࡣ㸪⏫࡟ࡣᅗ 4 ࡟♧ࡉࢀࡿࠕࡑࡽࡗࡁ࣮ࠖ࡜࿧ࡤዿ᛹࡜▼ࡢ⏬ീࡢ࢝ࢸࢦࣜศࡅࢆᐇ᪋ࡋࡓୖࢀࡿ㣕⾜ᶵࢆࣔࢳ࣮ࣇ࡜ࡋࡓࡈᙜᆅ࢟ࣕࣛࡀ࡛ኚ᥮ࢆ⾜ࡗ࡚࠸ࡿࠋຍ࠼࡚㸪࢔ࣥࢣ࣮ࢺㄪ᪤࡟Ꮡᅾࡋ࡚࠸ࡿࡀ㸪ࡑࡢㄆ▱ᗘࡣప࠸ࠋࡑࡢᰝࢆ⾜࠸㸪ຠᯝⓗ࡞ዿ᛹⏬ീࡀ⏕ᡂࡉࢀࡓ࢝ࡓࡵ㸪᪂ࡓ࡞ࡈᙜᆅ࢟ࣕࣛ࡜ࡋ࡚㸪኱✵⏫ࡢ≉ࢸࢦࣜ࡟ࡘ࠸᳨࡚ドࢆ⾜ࡗ࡚࠸ࡿࠋ⏘ရ࡛࠶ࡿࢩࢪ࣑ࡢ≉ᚩࢆ᭷ࡍࡿࡈᙜᆅ࢟ࣕ᭦࡟㸪GAN ࢆ⏝࠸ࡓࡺࡿ࢟ࣕࣛࡢ⏕ᡂ࡟ࣛࡢ⏕ᡂࢆ⾜࠺ࠋ᭦࡟㸪⌧ᅾࡢࡈᙜᆅ࡛࢟ࣕࣛ㛵ࡍࡿ◊✲ࡶᐇ᪋ࡉࢀ࡚࠸ࡿࠋⴗཎࡽ࠶ࡿࠕࡑࡽࡗࡁ࣮ࠖ࡜ࡢẚ㍑ࢆ⾜࠺ࡓࡵ㣕⾜ᶵ

㸦2020㸧ࡣ㸪CGAN ࢆ฼⏝ࡍࡿࡇ࡜࡟ࡼࡾ

ࡢ≉ᚩࢆ᭷ࡍࡿࡈᙜᆅ࢟ࣕࣛࡢ⏕ᡂࡶ⾜࠺ࠋ᪂ࡓ࡞ࡺࡿ࢟ࣕࣛࡢ⏕ᡂࢆヨࡳ࡚࠸ࡿࠋࡇࡇ࡛ࡣ㸪ࡺࡿ࢟ࣕࣛࢢࣛࣥࣉࣜ࡟ฟሙࡋࡓࡺࡿ ࢹ࣮ࢱ཰㞟࢟ࣕࣛࢆ≉ᚩࡈ࡜࡟ᡭື࡛ࢡࣛࢫࢱ࣮ศ㢮ࡋ㸪ྛࢡࣛࢫࢱ࣮ࡢ≉ᚩࢆᣢࡘࡺࡿ࢟ࣕࣛࡢᏛ⩦࡟⏝࠸ࡿࡓࡵ㸪ࡺࡿ࢟ࣕࣛ㸪ࢩࢪ࣑㸪㣕⏕ᡂࢆ⾜ࡗ࡚࠸ࡿࠋࡋ࠿ࡋ࡞ࡀࡽ㸪ࡇࡢ◊✲⾜ᶵࡢ⏬ീࢆࡑࢀࡒࢀ཰㞟ࡋࡓࠋࡲࡎ㸪ࡺࡿ࡛࢟ࡣ㸪᪂ࡓ࡞ࡺࡿ࢟ࣕࣛࢆ⏕ᡂࡍࡿࡇ࡜ࡢࡳࣕࣛ⏬ീࡣ㸪ࡺࡿ࢟ࣕࣛࢢࣛࣥࣉࣜࡢࢧ࢖ࢺࢆ┠ⓗ࡜ࡋ࡚࠸ࡿࡓࡵ㸪ࡈᙜᆅ࢟ࣕࣛ࡟ᚲせ࠿ࡽ 1260 ᯛࢆ཰㞟ࡋࡓࠋ཰㞟ࡋࡓ⏬ീࡢ⫼ᬒ࡜ࡉࢀࡿᆅᇦᛶࡣ⪃៖ࡉࢀ࡚࠸࡞࠸ࠋᮏ◊✲ࢆᡭື࡟࡚๐㝖ࡋ㸪256256 ࣆࢡࢭࣝ࡟ࢧ࢖

࡛ࡣ㸪CycleGAN ࢆ฼⏝ࡋ㸪≉⏘ရࡸほගྡࢬࢆኚ᭦ࡋࡓࠋ

ᡤ࡞࡝ࡢ⏬ീ࡜ࡺࡿ࢟ࣕࣛ⏬ീࡢࢫࢱ࢖ࣝࢆ

ࢩࢪ࣑ࡢ⏬ീ࡟ࡘ࠸࡚ࡣ㸪Google ࡢ⏬ീ᳨ኚ᥮ࡍࡿࡇ࡜࡛㸪ᆅᇦᛶࢆᣢࡘࡈᙜᆅ࢟ࣕࣛ

⣴ࢆ⏝࠸࡚」ᩘࡢࢩࢪ࣑ࡀ෗ࡿ 50 ᯛࡢ⏬ീࡢ⏕ᡂࢆヨࡳࡿࠋࢆ཰㞟ࡋࡓࠋᡭື࡟ࡼࡾ཰㞟⏬ീࡢ⫼ᬒ๐㝖ࢆ⾜࠸㸪256256 ࣆࢡࢭࣝ࡟ࣜࢧ࢖ࢬࡋࡓࠋ
ࡇࡢ⏬ീ࡟ᑐࡋ࡚㸪ୖୗᕥྑ཯㌿࡟ࡼࡿỈቑ㸬 CycleGAN ࢆ⏝࠸ࡓᆅᇦ≉ᛶࢆᣢࡘࡋฎ⌮ࢆ㐺⏝ࡋ㸪800 ᯛࡢᏛ⩦⏝ࢹ࣮ࢱࢆస
ᡂࡋࡓࠋ㸫5㸫⏎,⏎ᬏ࣊Ƕӵ੯ͺ፳ǶǶࢼчᅗ Ꮫ⩦⏝ࢹ࣮ࢱ౛ᅗ  ࡑࡽࡗࡁ࣮㸦ฟ඾㸸኱✵⏫ :HE ࢧ࢖ࢺ㸧᭦࡟㸪」ᩘࡢࢩࢪ࣑ࡀ෗ࡿ⏬ീ࠿ࡽࢩࢪ࣑

 CycleGAN ࡢᵓᡂࢆ୍ࡘࡎࡘษࡾฟࡋ㸪ࢩࢪ࣑༢యࡢᏛ⩦⏝⏬

ᅗ 6 ࡟ᮏ◊✲࡟࠾ࡅࡿ CycleGAN ࡢᵓᡂࢆീࢆసᡂࡋࡓࠋࡇࡢษࡾฟࡋฎ⌮࡟ࡼࡾ 32 ᯛ♧ࡍࠋᅗ࡟♧ࡉࢀࡿࡼ࠺࡟㸪2 ࡘࡢ㆑ูჾ㸪ཬࡢࢩࢪ࣑༢య⏬ീࢆసᡂࡋࡓࠋࡇࡢ 32 ᯛ࡟ᑐࡧ㸪2 ࡘࡢ⏕ᡂჾ࠿ࡽᵓᡂࡉࢀࡿࠋ㆑ูჾࡣ㸪ࡋᕥྑ཯㌿㸪ゅᗘኚ᭦ࡢỈቑࡋฎ⌮ࢆ㐺⏝ࡋ㸪
874 ᯛࡢᏛ⩦ࢹ࣮ࢱࢆసᡂࡋࡓࠋ

4 ᒙࡢ␚ࡳ㎸ࡳᒙ࡜඲⤖ྜᒙ࠿ࡽᵓᡂࡉࢀࡿࠋᣓᘼෆࡣࣇ࢕ࣝࢱᩘ㸪ཬࡧ㸪ࣇ࢕ࣝࢱࢧ࢖ࢬࢆࡲࡓ㸪㣕⾜ᶵࡢ⏬ീ࡟ࡘ࠸࡚ࡣࢩࢪ࣑ࡢ⏬

⾲ࡋ࡚࠸ࡿࠋࡲࡓ㸪␚ࡳ㎸ࡳᒙ࡛ࡣ Reflectionീ࡜ྠᵝ࡟ Google ࡢ⏬ീ᳨⣴ࢆ⏝࠸㸪㣕⾜ᶵ

Padding ࢆ⏝࠸ࡿࠋ␚ࡳ㎸ࡳฎ⌮ࡢᚋ࡟㸪άࡢഃ㠃ࡀ෗ࡗ࡚࠸ࡿ⏬ീ࡟ࡘ࠸࡚ 227 ᯛ㸪㣕ᛶ໬㛵ᩘ࡜ࡋ࡚ LeakyReLU ࢆ⏝࠸࡚࠸ࡿࠋ⾜ᶵࡢṇ㠃ࡀ෗ࡗ࡚࠸ࡿ⏬ീ࡟ࡘ࠸࡚ 76 ᯛ⏕ᡂჾࡣ 3 ᒙࡢ␚ࡳ㎸ࡳᒙ㸪
9 ᒙࡢ Residualࡢ⏬ീࢆ཰㞟ࡋࡓࠋࡇࢀࡽ཰㞟⏬ീ࡟ᑐࡋ࡚㸪⫼ᬒ㝖ཤ㸪256256 ࣆࢡࢭࣝ࡬ࡢࣜࢧ࢖ࢬࢆ

Block㸪2 ᒙࡢ㌿⨨␚ࡳ㎸ࡳᒙ㸪ཬࡧ㸪ฟຊᒙ࠿ࡽᵓᡂࡉࢀࡿࠋ␚ࡳ㎸ࡳᒙ࡟ࡼࡿ␚ࡳ㎸ࡳ⾜ࡗࡓࠋ᭦࡟㸪ഃ㠃⏬ീ࡟ᑐࡋ࡚ࡣୖୗᕥྑ཯ฎ⌮ࡢᚋ㸪Residual Block ࡟ࡼࡿ 33 ࣆࢡ㌿ࡢỈቑࡋฎ⌮ࢆ㸪ṇ㠃ࡢ⏬ീ࡟ᑐࡋ࡚ࡣୖࢭࣝࡢࣇ࢕ࣝࢱࢆ⏝࠸ࡓ␚ࡳ㎸ࡳࢆ⾜࠺ࠋḟୗᕥྑ཯㌿࡜ゅᗘኚ᭦ࡢỈቑࡋฎ⌮ࢆ㐺⏝ࡋ㸪ഃ㠃⏬ീ 814 ᯛ࡜ṇ㠃⏬ീ 1140 ᯛࡢᏛ⩦ࢹ࡟ 33 ࣆࢡࢭࣝࡢࣇ࢕ࣝࢱࢆ⏝࠸ࡓ㌿⨨␚ࡳ㎸ࡳࢆ 2 ᒙ㐺⏝ࡍࡿࠋࡲࡓ㸪Residual Block࣮ࢱࢆ⏕ᡂࡋࡓࠋᅗ 5 ࡟Ꮫ⩦⏝ࢹ࣮ࢱࡢ౛ࢆ௨እࡢྛᒙ࡟࠾ࡅࡿฎ⌮ࡢᚋ㸪άᛶ໬㛵ᩘ࡜♧ࡍࠋᅗ࡛ࡣ㸪1 ẁ┠ࡀࡺࡿ࢟ࣕࣛ⏬ീ㸪2 ẁࡋ࡚ ReLU ࡟ࡼࡿฎ⌮ࢆ㐺⏝ࡍࡿࠋฟຊᒙ࡛┠ࡀ」ᩘࡢࢩࢪ࣑⏬ീ㸪3 ẁ┠ࡀࢩࢪ࣑༢య

ࡣ㸪άᛶ໬㛵ᩘ࡜ࡋ࡚ tanh ࢆ౑⏝ࡍࡿࠋ⏕⏬ീ㸪4 ẁ┠ࡀ㣕⾜ᶵࡢṇ㠃㸪ཬࡧ㸪ഃ㠃ࡢ⏬

ᡂჾ࡟࠾ࡅࡿ␚ࡳ㎸ࡳᒙ࡟࠾࠸࡚ࡶ㸪ീࢆ♧ࡋ࡚࠸ࡿࠋ

Reflection Padding ࡀ㐺⏝ࡉࢀࡿࠋ⏬ീ⏕ᡂࡢὶࢀࢆࢩࢪ࣑⏬ീࡢሙྜ࡟࠾࠸

㸫6㸫⏎.⏎

I´PE^ ˂ᅎȥɜ‫ݖܩ‬႕ਸ˂୫ɥɆয়‫͒͝˦ܩ‬ɺᅊଁ

CycleGAN ࢆᐇ⿦ࡍࡿࡓࡵ࡟㸪ᶵᲔᏛ⩦⏝ࡢࣛ ࢖ ࣈ ࣛ ࣜ ࡛ ࠶ ࡿ Pytorch ࢆ ⏝ ࠸ ࡓ ࠋગ૗ఌ઺൸$েਛஓ‫ق‬%‫ڀ‬$‫ك‬૗ఌ઺൸%‫ڀ‬$૗ఌ઺൸$‫ڀ‬%েਛஓ‫ق‬$‫ڀ‬%‫ك‬઺൸%௙શஓ‫ق‬%‫ك‬ʁड़ྙࣉ࡟ࡼࡾ㛤ⓎࡉࢀࡓᶵᲔᏛ⩦ࡢࡓࡵࡢࣛ࢖ࣈ࡛ࣛࣜ࠶ࡿࠋᮏ CycleGAN ࡟࠾ࡅࡿࣁ࢖ࣃ࣮ࣃ࣓࣮ࣛࢱ࡜ࡋ࡚㸪Ꮫ⩦ᅇᩘ 200 ࢚࣏ࢵࢡ㸪Ꮫ⩦⋡ 0.0002㸪

઺൸$ગ૗ఌ઺൸%ʁ೘ྙPytorch ࡣ Facebook ࡢேᕤ▱⬟◊✲ࢢ࣮ࣝ௙શஓ‫ق‬$‫ك‬ࣂࢵࢳࢧ࢖ࢬ 1 ࢆ᥇⏝ࡋࡓࠋࡲࡓ㸪ྠ୍ᛶᦆ௙શஓেਛஓኻࡢኚ໬㔞ࢆ 0.8㸪ࢧ࢖ࢡ୍ࣝ㈏ᛶᦆኻࡢኚ໬

႙ा੢ाಽ‫ك¼؜ق‬႙ा੢ाಽ‫ك¼؜ق‬㔞ࢆ 1.0 ࡜ࡋࡓࠋࡇࢀࡽࣃ࣓࣮ࣛࢱࡣࣄ࣮ࣗ

LeakyReLU

ReLU
ReLU

LeakyReLU

႙ा੢ाಽ‫ك¼؜ق‬ReLU

႙ा੢ाಽ‫ك¼؜ق‬5HVEORFN 

LeakyReLU

ૡ઼႙੢ाಽ‫ك¼؜ق‬႙ा੢ाಽ‫ك¼؜ق‬ReLU

LeakyReLU

ࣜࢫࢸ࢕ࢵࢡ࡟ࡼࡾỴᐃࡋࡓࡶࡢ࡛࠶ࡿࠋ

႙ा੢ाಽ‫ك¼؜ق‬႙ा੢ाಽ‫ك¼؜ق‬ ⏬ീ⏕ᡂ⤖ᯝ
ᅗ 7 ࡟ࢩࢪ࣑⏬ീ࠿ࡽࡺࡿ࢟ࣕࣛ⏬ീ࡬ࡢૡ઼႙੢ाಽ‫ك¼؜ق‬
ReLU

৸੥়ಽ

ኚ᥮⤖ᯝ౛㸪ᅗ 8 ࡟㣕⾜ᶵ⏬ീ࠿ࡽࡺࡿ࢟ࣕলৡಽ

ࣛ࡬ࡢኚ᥮⤖ᯝ౛ࢆ♧ࡍࠋࡇࢀࡽኚ᥮ࡢࡓࡵᅗ  &\FOH*$1 ࡢᵓᡂࡢᏛ⩦࡟ࡣ㸪⣙ 26 ᫬㛫ࢆせࡋࡓࠋ࡚♧ࡍࠋࢩࢪ࣑⏬ീ B ࡣ⏕ᡂჾ㸦BЍA㸧࡜㆑᪂ࡓ࡞ࡈᙜᆅ࢟ࣕࣛࡢཧ⪃࡜࡞ࡿ⏬ീ࡜ࡋูჾ B ࡟ධຊࡉࢀ㸪⏕ᡂჾ࡛ࡣኚ᥮⏬ീ㸦B࡚ࡣ㸪≉⏘ရ࡞࡝ࡢၟᴗᛶ㸪ほගᆅࡸࢩࣥ࣎ࣝЍA㸧࡬ࡢኚ᥮ࡀ⾜ࢃࢀࡿࠋኚ᥮ࡀ⾜ࢃࢀࡓ⏬

࡞࡝ࡢほගᛶࢆ⥔ᣢࡋࡘࡘ㸪ࡺࡿ࢟ࣕࣛ࡟ඹ

ീ࡟ᑐࡋ࡚㆑ูჾ A ࡟ࡼࡿ㆑ูࢆ⾜࠺ࠋࡲࡓ㸪㏻ࡍࡿ≉ᚩ࡛࠶ࡿᡭ㊊㸪┠㰯㸪⩚᰿࡜࠸ࡗࡓືኚ᥮⏬ീ㸦BЍA㸧ࡣ⏕ᡂჾ㸦AЍB㸧࡬ධຊ㸪≀ⓗ≉ᚩࢆᣢࡘࡇ࡜ࡀᚲせ࡛࠶ࡿ࡜⪃࠼ࡿ㸬෌ኚ᥮ࡉࢀ㸪ඖ⏬ീ B ࡟㏆࠸≧ែ࡬෌ኚ᥮ࡉ

ᅗ 7 ࡢ⤖ᯝ࠿ࡽ㸪ࢩࢪ࣑ࡢሢࡢ⏬ീ࠿ࡽࡢࢀࡿࠋࡺࡿ࢟ࣕࣛ⏬ീ A ࡟ࡘ࠸࡚ࡶྠᵝࡢὶኚ᥮࡟࠾࠸࡚ࡣ㸪඲య࡜ࡋ࡚ࡢሢࡢᙧ≧ࡀ⥔ࢀࡢฎ⌮ࡀ⾜ࢃࢀ㸪ࡺࡿ࢟ࣕࣛ⏬ീࡣࢩࢪ࣑ᣢࡉࢀ㸪ࡑࡢෆ㒊࡟ࡺࡿ࢟ࣕࣛࡢࢫࢱ࢖ࣝࡀࡢ⏬ീ࡬ኚ᥮㸪㆑ูჾ࡟ධຊ㸪෌ኚ᥮ࡀ⾜ࢃࢀ཯ᫎࡉࢀ㸪࠿ࡘ㸪ᡭ㊊ࡢࡼ࠺࡞ࡶࡢࡀ௜ᖏࡋ࡚ࡿࠋࡇࢀࡽࡢฎ⌮࡟ࡼࡗ࡚ࢩࢪ࣑ࡢ≉ᚩࢆ᭷࠸ࡿ㸬ࡋ࠿ࡋ࡞ࡀࡽ㸪ሢࡢሙྜࡇࡢᙧ≧ࡀࢩࢪࡍࡿ᪂ࡓ࡞ࡈᙜᆅ࢟ࣕࣛ⏬ീ㸪ཬࡧ㸪ࡺࡿ࣑࡛࢟ࣕ࠶ࡿ࡜ㄆ㆑ࡉࢀ࡙ࡽ࠸㸬ࡇࡢࡓࡵ㸪ࢩࢪ࣑ࣛࡢ≉ᚩࢆ᭷ࡍࡿࢩࢪ࣑⏬ീࡀ⏕ᡂࡉࢀࡿࠋ

ࡢ≉ᚩࢆᣢࡘࡈᙜᆅ࢟ࣕࣛ࡜ࡋ࡚ࡣ᭷ຠᛶࡢ
ప࠸⏬ീࡀ⏕ᡂࡉࢀࡓࠋ୍᪉㸪༢యࡢࢩࢪ࣑ࡢ
⏬ീࢆኚ᥮ࡋࡓሙྜ࡟ࡣ㸪ࢩࢪ୍࣑ࡘࡢᙧ≧㸬 ⏬ീ⏕ᡂᐇ㦂≉ᚩࡀ⥔ᣢࡉࢀ㸪ෆ㒊࡟┠㰯ⓗ࡞せ⣲㸪᭦࡟㸪
ᡭ㊊ⓗ࡞せ⣲ࢆᣢࡗࡓࡺࡿ࢟ࣕࣛ⏬ീࡀ⏕ᡂ ᐇ㦂⎔ቃ࡜ࣃ࣓࣮ࣛࢱࡉࢀ࡚࠸ࡿࠋࡇࢀࢆ࣮࣋ࢫ࢔࢖ࢹ࢕࢔࡜ࡋ࡚ᮏ◊✲࡟࠾ࡅࡿ CycleGAN ࢆᐇ⾜ࡍࡿࡓࡵ⏝࠸ࡿࡇ࡜࡛㸪᪂ࡓ࡞ࡈᙜᆅ࢟ࣕࣛࢆⓎ᝿࡛࡟㸪බ❧ࡣࡇࡔ࡚ᮍ᮶኱Ꮫࡢ GPU ࢡࣛࢫࢱࡁࡿྍ⬟ᛶࡀ࠶ࡿࡶࡢ࡜⪃࠼ࡿࠋࡲࡓ㸪༢యࡢ࣐ ࢩ ࣥ 㸦 OS: Cent OS7, CPU: Intel Xeonࢩࢪ࣑⏬ീࢆ⏝࠸ࡓ⏬ീ⏕ᡂᐇ㦂࡛ࡣ㸪1000

Silver4215

ᯛࡢ⏬ീࢆ⏕ᡂࡋࡓࡀ㸪ࡑࡢ୰࡛ࢩࢪ࣑ࡢᙧ

Processor,GPU:GeForce

RTX2080 TI-11G㸲㸧ࢆ฼⏝ࡋࡓࠋࡲࡓ㸪
㸫7㸫⏎0⏎ᬏ࣊Ƕӵ੯ͺ፳ǶǶࢼч३४঑‫ق‬჋‫ॅڀك‬ॊय़কছఄষਃ‫ق‬ડએ‫؞‬ਫએ‫ॅڀك‬ॊय़কছᅗ  㣕⾜ᶵ⏬ീࡢኚ᥮⤖ᯝ౛३४঑‫౐ق‬৬‫ॅڀك‬ॊय़কছ࡛ࣕࣛ࠶ࡿࠕࡑࡽࡗࡁ࣮ࠖࡣ㣕⾜ᶵࡑࡢࡲࡲࡢ
࢟ࣕࣛࢡࢱ࣮࡛࠶ࡿࡀ㸪ᮏ◊✲࡛⏕ᡂࡉࢀࡓ࢟ࣕࣛࢡࢱ࣮ࡣ㣕⾜ᶵᙧ≧࡜ࡺࡿ࢟ࣕࣛࡢ≉ᚩࢆᣢࡘ࢟ࣕࣛࢡࢱ࣮ࡀ⏕ᡂࡉࢀ࡚࠸ࡿࡇ࡜
ࡀࢃ࠿ࡿࠋ ࡋ࠿ࡋ࡞ࡀࡽ㸪ࢩࢪ࣑㸪ཬࡧ㸪㣕⾜ᶵࡢ୧ኚ᥮࡟࠾࠸࡚㸪ࢫࢱ࢖ࣝࡢኚ᥮ࡀ⾜ࢃࢀࡓࡀ㸪ࡺࡿ࢟ࣕࣛ࡟࠾ࡅࡿᡭ㊊┠㰯ࡢ≉ᚩࡀ᫂☜࡟཯ᫎࡉࢀ࡚࠸ࡿ࡜ࡣゝ࠸㞴࠸ኚ᥮⤖ᯝ࡜࡞ࡗࡓࠋ
ᮏ◊✲࡛ࡣ㸪ࡺࡿ࢟ࣕࣛ඲య࡜ࡋ࡚ࡢ≉ᚩࢆᤊ࠼㸪ࡑࢀࡽࡀ≉⏘ရ࡞࡝࡟཯ᫎࡉࢀࡿࡇ࡜ࢆᮇᚅࡋࡓࡀ㸪᥇⏝ࡋࡓࡺࡿ࢟ࣕࣛࡢ✀㢮ࡀከࡃ㸪ࡑࡢ୰࡛ඹ㏻ࡢ≉ᚩࡀぢ࠸ࡔࡏ࡞࠿ࡗࡓࡇ࡜ࡀཎᅉ࡛࠶ࡿ࡜⪃࠼ࡿࠋ㛗ᕊ㸦2020㸧ࡸⴗཎࡽ㸦2020㸧ࡢ◊✲࡟࠶ࡿࡼ࠺࡟㸪ࡺࡿ࢟ࣕࣛࢆ≉ᚩẖ࡟ศ㢮ࡋ㸪Ꮫ⩦ࡉࡏࡿࡇ࡜࡟ᅗ  ࢩࢪ࣑⏬ീࡢኚ᥮⤖ᯝ౛ࡼࡾ᫂♧ⓗ࡞ࡺࡿ࢟ࣕࣛࡢ≉ᚩࢆ཯ᫎࡋࡓࡈ≧ࢆ⥔ᣢࡋ㸪ࡺࡿ࢟ࣕࣛ࡜ࡋ࡚ࡢᡭ㊊┠㰯ࡢᙜᆅ࢟ࣕࣛࡀ⏕ᡂࡉࢀࡿྍ⬟ᛶࡀ࠶ࡿ࡜⪃࠼ࡼ࠺࡞ື≀ⓗ࡞≉ᚩࢆ᭷ࡋࡓ⏬ീࡣ 568 ᯛ࡜ࡿࠋ࡞ࡗࡓ㸬 ୍᪉㸪ᅗ 8 ࡟♧ࡉࢀࡿ㣕⾜ᶵ⏬ീࡢኚ᥮⤖ᯝ౛࡛ࡶ㸪㣕⾜ᶵࡢᙧ≧≉ᚩࡀ⥔ᣢࡉࢀࡓࡺࡿ࢟ࣕࣛࡀ⏕ᡂࡉࢀࡓࠋ኱✵⏫ࡢබᘧࡺࡿ࢟
㸫8㸫⏎2⏎I´PE^ ˂ᅎȥɜ‫ݖܩ‬႕ਸ˂୫ɥɆয়‫͒͝˦ܩ‬ɺᅊଁ㸬 ࠾ࢃࡾ࡟Mirza, Bing Xu, David Warde-Farley,
Sherjil Ozair, Aaron Courvile, Yoshuaᮏㄽ࡛ࡣ㸪CycleGAN ࢆ⏝࠸ࡓᆅᇦ≉ᛶࢆ

Bengio㸦2014㸧Generative Adversarialᣢࡘࡈᙜᆅ࢟ࣕࣛࡢ⏕ᡂࢆᥦ᱌ࡋࡓࠋࡈᙜᆅ

Nets, https://papers.nips.cc/paper/5423-

࢟ࣕࣛࢆ⏕ᡂࡍࡿᑐ㇟ᆅᇦ࡜ࡋ࡚㸪໭ᾏ㐨኱

generative-adversarial-nets.pdf 㸦2020 ᖺ 8✵⏫ࢆ୍౛࡜ࡋ࡚ྲྀࡾୖࡆ㸪኱✵⏫ࡢ≉⏘ရ

᭶ 16 ᪥࢔ࢡࢭࢫ㸧ࠋ࡛࠶ࡿ㸪ࢩࢪ࣑ࡢ≉ᚩࢆ᭷ࡍࡿࡈᙜᆅ࢟ࣕࣛᰩཎ⪽㸪୰ᓥ⠜㸪ᅜᯇᩔ㸦2020㸧࠸࠿࡟ࡋ࡚ࡢ⏕ᡂࢆ⾜ࡗࡓࠋ᭦࡟㸪⌧ᅾ㸪኱✵⏫ࡢබᘧࡺࠕࡥ࠸࡝ࢇࠖ⏕ࡲࢀࡓࡢ࠿?, ேᕤ▱⬟Ꮫࡿ࡛࢟ࣕࣛ࠶ࡿ㣕⾜ᶵࢆࣔࢳ࣮ࣇ࡜ࡋࡓࡑࡽ఍ㄅ, 35 ᕳ, 3 ྕ, pp.410-417ࠋ

ࡗࡁ࣮࡜ࡢẚ㍑ࢆ⾜࠺ࡓࡵ㸪㣕⾜ᶵࡢ≉ᚩࢆ

Jun-Yan Zhu㸪Taesung Park㸪Phillip Isola㸪ᣢࡘࡈᙜᆅ࢟ࣕࣛࡢ⏕ᡂࢆ⾜ࡗࡓࠋAlexei A.Efros㸦2017㸧Unpaired Image-to-⏬ീ⏕ᡂ⤖ᯝ࠿ࡽ㸪ࢩࢪ࣑༢యࡢ⏬ീࢆࡺ

Image Translationusing Cycle-Consistentࡿ࢟ࣕࣛ࡟ኚ᥮ࡋࡓ㝿࡟㸪ࢩࢪ࣑ࡢᙧ≧ࢆ᭷Adversarial Networksࡍࡿࡺࡿ࢟ࣕࣛࡀ⏕ᡂࡉࢀࡓࠋࡲࡓ㸪㣕⾜ᶵ⏬

https://arxiv.org/pdf/1703.10593.pdf㸦2020ീࡢኚ᥮࡟࠾࠸࡚ࡶ㣕⾜ᶵࡢᙧ≧≉ᚩࢆᣢࡘᖺ 8 ᭶ 16 ᪥࢔ࢡࢭࢫ㸧ࠋࡺࡿ࢟ࣕࣛࡢ⏕ᡂࡀ☜ㄆࡉࢀࡓࠋࡇࢀࡽ࠿ࡽ㸪

M. Mirza and S. Osindero㸦2014㸧Conditionalᮏ◊✲ࡢ⤖ᯝࡣ㸪᪂ࡓ࡞ࡈᙜᆅ࢟ࣕࣛࢆసᡂ

generative adversarial nets, arXiv preprintࡍࡿ㝿ࡢⓎ᝿ᨭ᥼࡟ᑐࡋ࡚ຠᯝⓗ࡛࠶ࡿྍ⬟arXiv:1411.1784

ᛶࡀ♧ࡉࢀࡓࠋ

https://arxiv.org/pdf/1411.1784.pdf㸦2021ࡋ࠿ࡋ࡞ࡀࡽ㸪ኚ᥮⏬ീ࡟࠾࠸࡚ࡺࡿ࢟ࣕ

ᖺ 10 ᭶ 27 ᪥࢔ࢡࢭࢫ㸧ࠋࣛࡢ≉ᚩࢆຠᯝⓗ࡟࡜ࡽ࠼ࡿࡇ࡜ࡀ࡛ࡁ࡞࠿

A. Radford, L. Metz and S. Chintala㸦2015㸧ࡗࡓࠋࡇࢀࡣ㸪ࡺࡿ࢟ࣕࣛࡢ≉ᚩࡈ࡜࡟ேⅭⓗUnsupervised representation learning with࡞ࢡࣛࢫࢱࣜࣥࢢࢆᐇ᪋ࡋࡓᚋ㸪Ꮫ⩦ࢆ⾜࠺

deep convolutional generative adversarialࡇ࡜࡟ࡼࡾゎỴ࡛ࡁࡿྍ⬟ᛶࡀ࠶ࡿࠋࡇࢀࡣ

networks, arXiv preprint arXiv:1511.06434

௒ᚋࡢㄢ㢟࡛࠶ࡿࠋ

https://arxiv.org/abs/1511.06434㸦2021 ᖺ10 ᭶ 27 ᪥࢔ࢡࢭࢫ㸧ࠋㅰ㎡༓ⴥዌ㡢㸪▼⏿ᏹ᫂㸦2019㸧CycleGAN ࢆ⏝ᮏ◊✲ࢆᐇ᪋ࡍࡿ࡟࠶ࡓࡾ㸪῝ᒙᏛ⩦⏝ࡢ࠸ࡓ㣗࡭࠿ࡅᩱ⌮⏬ീࡢ᚟ඖ㸪᝟ሗฎ⌮GPU ࢡࣛࢫࢱ࣐ࢩࣥࢆ฼⏝ࡉࡏ࡚㡬࠸ࡓ㸪Ꮫ఍➨ 81 ᅇ඲ᅜ኱఍ㅮ₇ㄽᩥ㞟㸪බ❧ࡣࡇࡔ࡚ᮍ᮶኱Ꮫ㕥ᮌᜨ஧ᩍᤵ࡟ᚰࡼࡾ

pp.179-180ࠋ

ឤㅰࢆ⏦ࡋୖࡆࡲࡍࠋ

㛗ᕊ▱ె㸦2020㸧CycleGAN ࢆ⏝࠸ࡓዿ᛹࡜
▼࡟ࡼࡿྜᡂ⏬ീࡢ⏕ᡂ㸪බ❧ࡣࡇࡔ࡚ᮍ᮶኱Ꮫ༞ᴗㄽᩥ㸦௧࿴ඖᖺᗘ㸧ࠋཧ⪃ᩥ⊩ⴗཎ♸ᕼ㸪⏣୰⪽ஂ㸦2020㸧ࡺࡿ GAN:ࡺࡿ᪥ᮏ㖟⾜⇃ᮏᨭᗑ㸦2013㸧ࡃࡲࣔࣥࡢ⤒῭ຠ࢟ࣕࣛࢆ⏕ᡂࡍࡿᩛᑐⓗ⏕ࢿࢵࢺ࣮࣡

ᯝ㸪https://www3.boj.or.jp/kumamoto/ࢡ㸪㟁Ꮚ᝟ሗ㏻ಙᏛ఍ᢏ⾡◊✲ሗ࿌㸪

tokubetsu_chosa/20131226kumamon.pdf

119 ᕳ㸪425 ྕ㸪pp.101-106ࠋ

㸦2021 ᖺ 8 ᭶ 13 ᪥࢔ࢡࢭࢫ㸧ࠋIan J.Goodfellow, Jean Pouget-Abadie, Mehdi㸫9㸫⏎4⏎