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\u30b9\u30e0\u30fc\u30b8\u30f3\u30b0\u306f\u5b9f\u88c5\u304c\u3088\u308a\u7c21\u5358\u3067\u3059\u304c\u3001\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0 \u30e9\u30d9\u30eb \u30b9\u30e0\u30fc\u30b8\u30f3\u30b0\u3067\u306f\u6700\u9069\u306a\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u9054\u6210\u3059\u308b\u305f\u3081\u306b\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u306e\u8abf\u6574\u304c\u5fc5\u8981\u306b\u306a\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<p>\u4ee5\u4e0b\u306f\u30012 \u7a2e\u985e\u306e\u30e9\u30d9\u30eb \u30b9\u30e0\u30fc\u30b8\u30f3\u30b0\u306e\u6bd4\u8f03\u3067\u3059\u3002<\/p>\n<table>\n<thead>\n<tr>\n<th>\u5074\u9762<\/th>\n<th>\u56fa\u5b9a\u30e9\u30d9\u30eb\u306e\u30b9\u30e0\u30fc\u30b8\u30f3\u30b0<\/th>\n<th>\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0 \u30e9\u30d9\u30eb \u30b9\u30e0\u30fc\u30b8\u30f3\u30b0<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u03b5\u5024<\/td>\n<td>\u5168\u4f53\u3092\u901a\u3057\u3066\u4e00\u5b9a<\/td>\n<td>\u713c\u304d\u306a\u307e\u3057\u307e\u305f\u306f\u8150\u98df<\/td>\n<\/tr>\n<tr>\n<td>\u8907\u96d1<\/td>\n<td>\u5b9f\u88c5\u304c\u7c21\u5358<\/td>\n<td>\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u8abf\u6574\u304c\u5fc5\u8981\u306a\u5834\u5408\u304c\u3042\u308a\u307e\u3059<\/td>\n<\/tr>\n<tr>\n<td>\u8f03\u6b63<\/td>\n<td>\u5fae\u8abf\u6574\u304c\u5c11\u306a\u3044<\/td>\n<td>\u6642\u9593\u306e\u7d4c\u904e\u3068\u3068\u3082\u306b\u5f90\u3005\u306b\u6539\u5584\u3055\u308c\u307e\u3057\u305f<\/td>\n<\/tr>\n<tr>\n<td>\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9<\/td>\n<td>\u5b89\u5b9a\u3057\u305f\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9<\/td>\n<td>\u3088\u308a\u826f\u3044\u7d50\u679c\u304c\u5f97\u3089\u308c\u308b\u53ef\u80fd\u6027<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u30e9\u30d9\u30eb\u30b9\u30e0\u30fc\u30b8\u30f3\u30b0\u306e\u4f7f\u3044\u65b9\u3068\u4f7f\u7528\u4e0a\u306e\u554f\u984c\u70b9\u3068\u305d\u306e\u89e3\u6c7a\u7b56\u3002<\/h2>\n<h3>\u30e9\u30d9\u30eb 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\u30b5\u30fc\u30d0\u30fc\u3092\u4f7f\u7528\u3057\u3066\u8ca0\u8377\u5206\u6563\u3092\u884c\u3044\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u8907\u6570\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u9593\u3067\u30e2\u30c7\u30eb\u63a8\u8ad6\u30ea\u30af\u30a8\u30b9\u30c8\u3092\u52b9\u7387\u7684\u306b\u5206\u6563\u3067\u304d\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u30ad\u30e3\u30c3\u30b7\u30e5\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c:<\/strong> \u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306b\u3088\u3063\u3066\u884c\u308f\u308c\u305f\u4e88\u6e2c\u3092\u30ad\u30e3\u30c3\u30b7\u30e5\u3067\u304d\u308b\u305f\u3081\u3001\u53cd\u5fa9\u7684\u306a\u30af\u30a8\u30ea\u306b\u5bfe\u3059\u308b\u5fdc\u7b54\u6642\u9593\u3068\u30b5\u30fc\u30d0\u30fc\u306e\u8ca0\u8377\u304c\u8efd\u6e1b\u3055\u308c\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n<p>\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u3068\u30e9\u30d9\u30eb \u30b9\u30e0\u30fc\u30b8\u30f3\u30b0\u306f\u72ec\u7acb\u3057\u3066\u52d5\u4f5c\u3057\u307e\u3059\u304c\u3001\u524d\u8005\u306f\u5805\u7262\u306a\u30c7\u30fc\u30bf\u53ce\u96c6\u3068\u3001\u30e9\u30d9\u30eb \u30b9\u30e0\u30fc\u30b8\u30f3\u30b0\u6280\u8853\u3092\u4f7f\u7528\u3057\u3066\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3055\u308c\u305f\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u52b9\u7387\u7684\u306a\u5c55\u958b\u3092\u78ba\u4fdd\u3059\u308b\u4e0a\u3067\u30b5\u30dd\u30fc\u30c8\u7684\u306a\u5f79\u5272\u3092\u679c\u305f\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<h2>\u95a2\u9023\u30ea\u30f3\u30af<\/h2>\n<p>\u30e9\u30d9\u30eb \u30b9\u30e0\u30fc\u30b8\u30f3\u30b0\u3068\u30c7\u30a3\u30fc\u30d7 \u30e9\u30fc\u30cb\u30f3\u30b0\u306b\u304a\u3051\u308b\u305d\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u8a73\u7d30\u306b\u3064\u3044\u3066\u306f\u3001\u6b21\u306e\u30ea\u30bd\u30fc\u30b9\u3092\u691c\u8a0e\u3059\u308b\u3053\u3068\u3092\u691c\u8a0e\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<ol>\n<li><a href=\"https:\/\/arxiv.org\/abs\/1512.00567\" target=\"_new\" rel=\"noopener nofollow\">\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30fc \u30d3\u30b8\u30e7\u30f3\u306e\u30a4\u30f3\u30bb\u30d7\u30b7\u30e7\u30f3 \u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u3092\u518d\u8003\u3059\u308b<\/a> \u2013 \u30e9\u30d9\u30eb \u30b9\u30e0\u30fc\u30b8\u30f3\u30b0\u3092\u7d39\u4ecb\u3057\u305f\u30aa\u30ea\u30b8\u30ca\u30eb\u306e\u7814\u7a76\u8ad6\u6587\u3002<\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/a-gentle-introduction-to-label-smoothing-fb96bc9156f0\" target=\"_new\" rel=\"noopener nofollow\">\u30e9\u30d9\u30eb \u30b9\u30e0\u30fc\u30b8\u30f3\u30b0\u306e\u512a\u3057\u3044\u5165\u9580<\/a> \u2013 \u521d\u5fc3\u8005\u5411\u3051\u306e\u30e9\u30d9\u30eb \u30b9\u30e0\u30fc\u30b8\u30f3\u30b0\u306b\u95a2\u3059\u308b\u8a73\u7d30\u306a\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3002<\/li>\n<li><a href=\"https:\/\/www.deeplearning.ai\/ai-notes\/regularization\/\" target=\"_new\" rel=\"noopener nofollow\">\u30e9\u30d9\u30eb\u306e\u30b9\u30e0\u30fc\u30b8\u30f3\u30b0\u306b\u3064\u3044\u3066<\/a> \u2013 \u30e9\u30d9\u30eb\u306e\u30b9\u30e0\u30fc\u30b8\u30f3\u30b0\u3068\u305d\u306e\u30e2\u30c7\u30eb\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3078\u306e\u5f71\u97ff\u306b\u3064\u3044\u3066\u306e\u5305\u62ec\u7684\u306a\u8aac\u660e\u3002<\/li>\n<\/ol>","protected":false},"featured_media":468749,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477793","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Label Smoothing<\/mark>","faq_items":[{"question":"What is Label Smoothing?","answer":"<p>Label smoothing is a regularization technique used in machine learning and deep learning models. It involves adding a small amount of uncertainty to the target labels during training to prevent overfitting and improve model generalization.<\/p>"},{"question":"How was Label Smoothing introduced?","answer":"<p>Label smoothing was first introduced in the research paper \"Rethinking the Inception Architecture for Computer Vision\" by Christian Szegedy et al. in 2016. The authors proposed it as a regularization method for large-scale image classification tasks.<\/p>"},{"question":"How does Label Smoothing work?","answer":"<p>Label smoothing modifies the traditional one-hot encoded target labels by distributing the probability mass among all classes. The true label is assigned a value slightly less than one, and the remaining probabilities are divided among other classes, introducing a sense of uncertainty during training.<\/p>"},{"question":"What are the types of Label Smoothing?","answer":"<p>There are two common types of label smoothing: fixed label smoothing and annealing label smoothing. Fixed label smoothing uses a constant value for uncertainty throughout training, while annealing label smoothing gradually decreases the uncertainty over time.<\/p>"},{"question":"How can I use Label Smoothing?","answer":"<p>To use label smoothing, modify the target labels before computing the loss during training. Prepare the dataset with one-hot encoded labels, choose a value for uncertainty (\u03b5), and convert the labels into softened labels with the probability distribution.<\/p>"},{"question":"What benefits does Label Smoothing offer?","answer":"<p>Label smoothing improves model robustness and calibration, making it less reliant on individual labels during prediction. It also handles noisy labels better and enhances generalization performance on unseen data.<\/p>"},{"question":"Are there any challenges with Label Smoothing?","answer":"<p>While label smoothing improves generalization, it might slightly reduce accuracy on the training set. Choosing an appropriate \u03b5 value requires experimentation, and implementation may need modification of the loss function.<\/p>"},{"question":"How can Proxy Servers be associated with Label Smoothing?","answer":"<p>Proxy servers are not directly related to label smoothing but can complement it. They can aid in diverse data collection, anonymizing user data, load balancing for model serving, and caching model predictions to optimize performance.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki\/477793","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki\/477793\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/media\/468749"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/media?parent=477793"}],"curies":[{"name":"\u3046\u30fc\u3093","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}