\uac01 \uc5c5\ub370\uc774\ud2b8\ub41c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uace0, \uadf8 \uacb0\uacfc\ub97c \uba54\ud0c0-\uadf8\ub77c\ub514\uc5b8\ud2b8\ub85c \uc0ac\uc6a9\ud558\uc5ec \ucd08\uae30 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc5c5\ub370\uc774\ud2b8\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n# MAML Pseudo-Code\nfor task in tasks:\n # Sample task data\n data = sample_data(task)\n # Update model parameters with few steps\n params_updated = model.update(params, data)\n # Evaluate and compute loss\n loss = compute_loss(params_updated, data)\n # Compute meta-gradient and aggregate\ncompute_meta_gradients(loss)\nupdate_initial_parameters(meta_gradients)\n<\/code><\/pre>\n3.2. Prototypical Networks<\/h3>\n
\n \ud504\ub85c\ud1a0\ud0c0\uc785 \ub124\ud2b8\uc6cc\ud06c\ub294 \uc8fc\ub85c few-shot learning\uc5d0 \uc0ac\uc6a9\ub418\uba70, \uac01 \ud074\ub798\uc2a4\uc758 \ud504\ub85c\ud1a0\ud0c0\uc785\uc744 \ud559\uc2b5\ud558\uc5ec \uc0c8\ub85c\uc6b4 \uc0d8\ud50c\uc774 \uc5b4\ub290 \ud074\ub798\uc2a4\uc5d0 \uc18d\ud558\ub294\uc9c0\ub97c \ud310\ub2e8\ud569\ub2c8\ub2e4. \uc774 \uc54c\uace0\ub9ac\uc998\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ubc29\uc2dd\uc73c\ub85c \uc791\ub3d9\ud569\ub2c8\ub2e4:\n <\/p>\n
\n- \ud6c8\ub828 \uc0d8\ud50c\uc5d0\uc11c \uac01 \ud074\ub798\uc2a4\uc758 \ud504\ub85c\ud1a0\ud0c0\uc785\uc744 \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/li>\n
- \uc0c8\ub85c\uc6b4 \uc0d8\ud50c\uc774 \uac01 \ud504\ub85c\ud1a0\ud0c0\uc785\uc5d0 \ub300\ud574 \uc5bc\ub9c8\ub098 \uac00\uae4c\uc6b4\uc9c0\ub97c \uacc4\uc0b0\ud558\uc5ec \ud074\ub798\uc2a4\ub97c \uacb0\uc815\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n
# Prototypical Networks Pseudo-Code\n# Given support_set and query_set\nprototypes = compute_prototypes(support_set)\nfor query in query_set:\n distances = compute_distances(query, prototypes)\n predicted_class = assign_class(distances)\n<\/code><\/pre>\n3.3. Reptile<\/h3>\n
\n Reptile\uc740 MAML\uacfc \uc720\uc0ac\ud558\uc9c0\ub9cc, \ub354 \uac04\ub2e8\ud55c \ucd5c\uc801\ud654 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc774 \uc54c\uace0\ub9ac\uc998\uc758 \uc8fc\uc694 \ubaa9\ud45c\ub294 \uc5ec\ub7ec \ud0dc\uc2a4\ud06c\uc758 \uacbd\ub7c9\ud654\ub97c \ud1b5\ud574 \ucd08\uae30 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\uc5ec \ub354 \uc27d\uace0 \ube60\ub974\uac8c \uc801\uc751\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc8fc\uc694 \ub2e8\uacc4\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:\n <\/p>\n
\n- \uc5ec\ub7ec \ud0dc\uc2a4\ud06c\ub97c \uc0d8\ud50c\ub9c1\ud569\ub2c8\ub2e4.<\/li>\n
- \uac01 \ud0dc\uc2a4\ud06c\uc5d0 \ub300\ud574 \uba87 \ubc88\uc758 \uacbd\ub7c9\ud654 \uc5c5\ub370\uc774\ud2b8\ub97c \uc218\ud589\ud569\ub2c8\ub2e4.<\/li>\n
- \uc774\ud6c4 \ubaa8\ub4e0 \ud0dc\uc2a4\ud06c\uc758 \uacb0\uacfc\ub97c \uc885\ud569\ud558\uc5ec \ucd08\uae30 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc5c5\ub370\uc774\ud2b8\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n
# Reptile Pseudo-Code\nfor task in tasks:\n params_updated = model.update(params, task_data)\nupdate_initial_parameters(params_updated)\n<\/code><\/pre>\n4. \uba54\ud0c0-\ub7ec\ub2dd\uc758 \uc751\uc6a9 \ubd84\uc57c<\/h2>\n
\n \uba54\ud0c0-\ub7ec\ub2dd\uc740 \ub2e4\uc591\ud55c \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c \uac00\uce58\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uadf8 \uc911 \uba87 \uac00\uc9c0\uc785\ub2c8\ub2e4:\n <\/p>\n
\n- \uc758\ub8cc \uc9c4\ub2e8:<\/strong> \ubbf8\ub9ac \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc0c8\ub85c\uc6b4 \uc99d\uc0c1\uc774\ub098 \uc9c8\ubcd1\uc5d0 \ub300\ud55c \uc9c4\ub2e8\uc744 \uc2e0\uc18d\ud558\uac8c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
- \uc790\uc728\uc8fc\ud589:<\/strong> \ucc28\ub7c9\uc774 \uc0c8\ub85c\uc6b4 \ub3c4\ub85c \ub610\ub294 \ud658\uacbd\uc5d0 \uc2e0\uc18d\ud558\uac8c \uc801\uc751\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc90d\ub2c8\ub2e4.<\/li>\n
- \ucd94\ucc9c \uc2dc\uc2a4\ud15c:<\/strong> \uc0ac\uc6a9\uc790\uc758 \ud589\ub3d9\uc744 \uae30\ubc18\uc73c\ub85c \uc0c8\ub85c\uc6b4 \ucd94\ucc9c\uc744 \ube60\ub974\uac8c \uc81c\uacf5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n
5. \uacb0\ub860<\/h2>\n
\n \uba54\ud0c0-\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c \ub370\uc774\ud130 \ubd80\uc871 \ubb38\uc81c\uc640 \uc2e0\uc18d\ud55c \uc801\uc751 \ub2a5\ub825\uc758 \ud544\uc694\uc131\uc744 \ud574\uacb0\ud558\ub294 \uac15\ub825\ud55c \ub3c4\uad6c\ub85c \uc790\ub9ac \uc7a1\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc591\ud55c \uba54\ud0c0-\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \ud65c\uc6a9\ud558\uc5ec \uac01\uae30 \ub2e4\ub978 \ud0dc\uc2a4\ud06c\uc5d0\uc11c\uc758 \ud559\uc2b5\uc744 \ucd5c\uc801\ud654\ud558\uba70, \uc0c8\ub85c\uc6b4 \ud0dc\uc2a4\ud06c\uc5d0 \ub300\ud55c \uc801\uc751\ub825\uc744 \ud5a5\uc0c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \uc800\ud76c\uac00 \uc55e\uc73c\ub85c\uc758 \ucd5c\ucca8\ub2e8 \uae30\uc220\uc744 \ubc1c\uc804\uc2dc\ud0a4\ub294 \ub370 \ud544\uc218\uc801\uc77c \uac83\uc785\ub2c8\ub2e4. \uba54\ud0c0-\ub7ec\ub2dd \uc5f0\uad6c\ub294 \uacc4\uc18d\ud574\uc11c \ubc1c\uc804\ud558\uace0 \uc788\uc73c\uba70, \uc55e\uc73c\ub85c\uc758 \uac00\ub2a5\uc131\uc740 \ubb34\uad81\ubb34\uc9c4\ud569\ub2c8\ub2e4.\n <\/p>\n
6. \ucc38\uace0 \ubb38\ud5cc<\/h2>\n
\n – Finn, C., Abbeel, P., & Levine, S. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. Proceedings of the 34th International Conference on Machine Learning<\/i>.
\n
\n – Snell, J., Swersky, K., & Zemel, R. (2017). Prototypical Networks for Few-shot Learning. Advances in Neural Information Processing Systems<\/i>.
\n
\n – Nichol, A., Achiam, J., & Schulman, J. (2018). On First-Order Meta-Learning Algorithms. Proceedings of the 2nd Workshop on Meta-Learning<\/i>.\n <\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\uba54\ud0c0-\ub7ec\ub2dd(Meta-Learning), \ud639\uc740 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6b0\ub294 \ud559\uc2b5(Learning to Learn)\uc740 \uc778\uacf5\uc9c0\ub2a5\uacfc \uae30\uacc4 \ud559\uc2b5\uc758 \ud55c \ubd84\uc57c\ub85c, \ubaa8\ub378\uc774 \uc0c8\ub85c\uc6b4 \uc791\uc5c5\uc744 \ucc44\ud0dd\ud560 \ub54c \ud544\uc694\ud55c \ub370\uc774\ud130\ub97c \ucd5c\uc18c\ud654\ud558\uba74\uc11c\ub3c4 \ud6a8\uc728\uc801\uc73c\ub85c \ud559\uc2b5\ud558\uac8c \ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uba54\ud0c0-\ub7ec\ub2dd\uc758 \uc8fc\ub41c \ubaa9\ud45c\ub294 \ub370\uc774\ud130 \ubd80\uc871, \ubaa8\ub378 \uacfc\uc801\ud569, \uadf8\ub9ac\uace0 \uc0c8\ub85c\uc6b4 \ud0dc\uc2a4\ud06c\uc5d0\uc758 \ube60\ub978 \uc801\uc751\uc744 \ud574\uacb0\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba54\ud0c0-\ub7ec\ub2dd\uc758 \uac1c\ub150, \uc911\uc694\uc131 \ubc0f \uc8fc\uc694 \uc54c\uace0\ub9ac\uc998\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \uba54\ud0c0-\ub7ec\ub2dd\uc758 \uac1c\ub150 \uba54\ud0c0-\ub7ec\ub2dd\uc740 \uae30\ubcf8\uc801\uc73c\ub85c … \ub354 \ubcf4\uae30 “35. \uba54\ud0c0-\ub7ec\ub2dd(Meta-Learning)\uc73c\ub85c \ube60\ub978 \uc801\uc751 \ud559\uc2b5\ud558\uae30, \uba54\ud0c0-\ub7ec\ub2dd\uc758 \uac1c\ub150\uacfc \uc8fc\uc694 \uc54c\uace0\ub9ac\uc998”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[213],"tags":[],"class_list":["post-40494","post","type-post","status-publish","format-standard","hentry","category-213"],"yoast_head":"\n35. \uba54\ud0c0-\ub7ec\ub2dd(Meta-Learning)\uc73c\ub85c \ube60\ub978 \uc801\uc751 \ud559\uc2b5\ud558\uae30, \uba54\ud0c0-\ub7ec\ub2dd\uc758 \uac1c\ub150\uacfc \uc8fc\uc694 \uc54c\uace0\ub9ac\uc998 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/40494\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"35. \uba54\ud0c0-\ub7ec\ub2dd(Meta-Learning)\uc73c\ub85c \ube60\ub978 \uc801\uc751 \ud559\uc2b5\ud558\uae30, \uba54\ud0c0-\ub7ec\ub2dd\uc758 \uac1c\ub150\uacfc \uc8fc\uc694 \uc54c\uace0\ub9ac\uc998 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\uba54\ud0c0-\ub7ec\ub2dd(Meta-Learning), \ud639\uc740 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6b0\ub294 \ud559\uc2b5(Learning to Learn)\uc740 \uc778\uacf5\uc9c0\ub2a5\uacfc \uae30\uacc4 \ud559\uc2b5\uc758 \ud55c \ubd84\uc57c\ub85c, \ubaa8\ub378\uc774 \uc0c8\ub85c\uc6b4 \uc791\uc5c5\uc744 \ucc44\ud0dd\ud560 \ub54c \ud544\uc694\ud55c \ub370\uc774\ud130\ub97c \ucd5c\uc18c\ud654\ud558\uba74\uc11c\ub3c4 \ud6a8\uc728\uc801\uc73c\ub85c \ud559\uc2b5\ud558\uac8c \ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uba54\ud0c0-\ub7ec\ub2dd\uc758 \uc8fc\ub41c \ubaa9\ud45c\ub294 \ub370\uc774\ud130 \ubd80\uc871, \ubaa8\ub378 \uacfc\uc801\ud569, \uadf8\ub9ac\uace0 \uc0c8\ub85c\uc6b4 \ud0dc\uc2a4\ud06c\uc5d0\uc758 \ube60\ub978 \uc801\uc751\uc744 \ud574\uacb0\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba54\ud0c0-\ub7ec\ub2dd\uc758 \uac1c\ub150, \uc911\uc694\uc131 \ubc0f \uc8fc\uc694 \uc54c\uace0\ub9ac\uc998\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \uba54\ud0c0-\ub7ec\ub2dd\uc758 \uac1c\ub150 \uba54\ud0c0-\ub7ec\ub2dd\uc740 \uae30\ubcf8\uc801\uc73c\ub85c … \ub354 \ubcf4\uae30 "35. \uba54\ud0c0-\ub7ec\ub2dd(Meta-Learning)\uc73c\ub85c \ube60\ub978 \uc801\uc751 \ud559\uc2b5\ud558\uae30, \uba54\ud0c0-\ub7ec\ub2dd\uc758 \uac1c\ub150\uacfc 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