{"id":478261,"date":"2023-08-09T09:29:53","date_gmt":"2023-08-09T09:29:53","guid":{"rendered":""},"modified":"2023-09-05T11:16:22","modified_gmt":"2023-09-05T11:16:22","slug":"one-shot-learning","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/one-shot-learning\/","title":{"rendered":"H\u1ecdc m\u1ed9t l\u1ea7n"},"content":{"rendered":"<p>H\u1ecdc m\u1ed9t l\u1ea7n \u0111\u1ec1 c\u1eadp \u0111\u1ebfn m\u1ed9t nhi\u1ec7m v\u1ee5 ph\u00e2n lo\u1ea1i trong \u0111\u00f3 m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o \u0111\u1ec3 nh\u1eadn d\u1ea1ng c\u00e1c \u0111\u1ed1i t\u01b0\u1ee3ng, m\u1eabu ho\u1eb7c ch\u1ee7 \u0111\u1ec1 t\u1eeb m\u1ed9t v\u00ed d\u1ee5 duy nh\u1ea5t ho\u1eb7c \u201cm\u1ed9t l\u1ea7n\u201d. Kh\u00e1i ni\u1ec7m n\u00e0y tr\u00e1i ng\u01b0\u1ee3c v\u1edbi c\u00e1c ph\u01b0\u01a1ng ph\u00e1p h\u1ecdc m\u00e1y th\u00f4ng th\u01b0\u1eddng, trong \u0111\u00f3 c\u00e1c m\u00f4 h\u00ecnh th\u01b0\u1eddng y\u00eau c\u1ea7u d\u1eef li\u1ec7u m\u1edf r\u1ed9ng \u0111\u1ec3 h\u1ecdc. Trong mi\u1ec1n d\u1ecbch v\u1ee5 m\u00e1y ch\u1ee7 proxy, h\u1ecdc m\u1ed9t l\u1ea7n c\u00f3 th\u1ec3 l\u00e0 m\u1ed9t ch\u1ee7 \u0111\u1ec1 ph\u00f9 h\u1ee3p, \u0111\u1eb7c bi\u1ec7t trong c\u00e1c b\u1ed1i c\u1ea3nh nh\u01b0 ph\u00e1t hi\u1ec7n s\u1ef1 b\u1ea5t th\u01b0\u1eddng ho\u1eb7c l\u1ecdc n\u1ed9i dung th\u00f4ng minh.<\/p>\n<h2>L\u1ecbch s\u1eed ngu\u1ed3n g\u1ed1c c\u1ee7a ph\u01b0\u01a1ng ph\u00e1p h\u1ecdc m\u1ed9t l\u1ea7n v\u00e0 s\u1ef1 \u0111\u1ec1 c\u1eadp \u0111\u1ea7u ti\u00ean v\u1ec1 n\u00f3<\/h2>\n<p>H\u1ecdc m\u1ed9t l\u1ea7n c\u00f3 ngu\u1ed3n g\u1ed1c t\u1eeb khoa h\u1ecdc nh\u1eadn th\u1ee9c, ph\u1ea3n \u00e1nh c\u00e1ch con ng\u01b0\u1eddi th\u01b0\u1eddng h\u1ecdc t\u1eeb nh\u1eefng v\u00ed d\u1ee5 \u0111\u01a1n l\u1ebb. Kh\u00e1i ni\u1ec7m n\u00e0y \u0111\u01b0\u1ee3c \u0111\u01b0a v\u00e0o khoa h\u1ecdc m\u00e1y t\u00ednh v\u00e0o \u0111\u1ea7u nh\u1eefng n\u0103m 2000.<\/p>\n<h3>M\u1ed1c th\u1eddi gian<\/h3>\n<ul>\n<li>\u0110\u1ea7u nh\u1eefng n\u0103m 2000: Ph\u00e1t tri\u1ec3n c\u00e1c thu\u1eadt to\u00e1n c\u00f3 kh\u1ea3 n\u0103ng h\u1ecdc t\u1eeb d\u1eef li\u1ec7u t\u1ed1i thi\u1ec3u.<\/li>\n<li>2005: M\u1ed9t b\u01b0\u1edbc quan tr\u1ecdng \u0111\u00e3 \u0111\u01b0\u1ee3c th\u1ef1c hi\u1ec7n v\u1edbi vi\u1ec7c xu\u1ea5t b\u1ea3n b\u00e0i b\u00e1o \u201cM\u00f4 h\u00ecnh ph\u00e2n c\u1ea5p Bayes \u0111\u1ec3 h\u1ecdc c\u00e1c lo\u1ea1i c\u1ea3nh t\u1ef1 nhi\u00ean\u201d c\u1ee7a Li Fei-Fei, Rob Fergus v\u00e0 Pietro Perona.<\/li>\n<li>T\u1eeb 2010 tr\u1edf \u0111i: T\u00edch h\u1ee3p ph\u01b0\u01a1ng ph\u00e1p h\u1ecdc m\u1ed9t l\u1ea7n trong c\u00e1c \u1ee9ng d\u1ee5ng AI v\u00e0 h\u1ecdc m\u00e1y kh\u00e1c nhau.<\/li>\n<\/ul>\n<h2>Th\u00f4ng tin chi ti\u1ebft v\u1ec1 H\u1ecdc m\u1ed9t l\u1ea7n. M\u1edf r\u1ed9ng ch\u1ee7 \u0111\u1ec1 H\u1ecdc m\u1ed9t l\u1ea7n<\/h2>\n<p>H\u1ecdc m\u1ed9t l\u1ea7n c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c chia th\u00e0nh hai l\u0129nh v\u1ef1c ch\u00ednh: M\u1ea1ng th\u1ea7n kinh t\u0103ng c\u01b0\u1eddng tr\u00ed nh\u1edb (MANN) v\u00e0 Si\u00eau h\u1ecdc t\u1eadp.<\/p>\n<ol>\n<li><strong>M\u1ea1ng th\u1ea7n kinh t\u0103ng c\u01b0\u1eddng b\u1ed9 nh\u1edb (MANN)<\/strong>: S\u1eed d\u1ee5ng b\u1ed9 nh\u1edb ngo\u00e0i \u0111\u1ec3 l\u01b0u tr\u1eef th\u00f4ng tin, cho ph\u00e9p h\u1ecd tham kh\u1ea3o th\u00f4ng tin n\u00e0y cho c\u00e1c c\u00f4ng vi\u1ec7c sau n\u00e0y.<\/li>\n<li><strong>Si\u00eau h\u1ecdc t\u1eadp<\/strong>: T\u1ea1i \u0111\u00e2y, m\u00f4 h\u00ecnh t\u1ef1 t\u00ecm hi\u1ec3u qu\u00e1 tr\u00ecnh h\u1ecdc t\u1eadp, cho ph\u00e9p m\u00f4 h\u00ecnh \u00e1p d\u1ee5ng ki\u1ebfn th\u1ee9c \u0111\u00e3 h\u1ecdc v\u00e0o c\u00e1c nhi\u1ec7m v\u1ee5 m\u1edbi, ch\u01b0a \u0111\u01b0\u1ee3c bi\u1ebft \u0111\u1ebfn.<\/li>\n<\/ol>\n<p>Nh\u1eefng k\u1ef9 thu\u1eadt n\u00e0y \u0111\u00e3 d\u1eabn \u0111\u1ebfn nh\u1eefng \u1ee9ng d\u1ee5ng m\u1edbi trong nhi\u1ec1u l\u0129nh v\u1ef1c kh\u00e1c nhau nh\u01b0 th\u1ecb gi\u00e1c m\u00e1y t\u00ednh, nh\u1eadn d\u1ea1ng gi\u1ecdng n\u00f3i v\u00e0 x\u1eed l\u00fd ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean.<\/p>\n<h2>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a vi\u1ec7c h\u1ecdc m\u1ed9t l\u1ea7n. C\u00e1ch h\u1ecdc m\u1ed9t l\u1ea7n ho\u1ea1t \u0111\u1ed9ng<\/h2>\n<ol>\n<li><strong>\u0110\u00e0o t\u1ea1o ng\u01b0\u1eddi m\u1eabu<\/strong>: M\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c hu\u1ea5n luy\u1ec7n v\u1edbi m\u1ed9t t\u1eadp d\u1eef li\u1ec7u nh\u1ecf \u0111\u1ec3 hi\u1ec3u c\u1ea5u tr\u00fac c\u01a1 b\u1ea3n.<\/li>\n<li><strong>Th\u1eed nghi\u1ec7m m\u00f4 h\u00ecnh<\/strong>: M\u00f4 h\u00ecnh sau \u0111\u00f3 \u0111\u01b0\u1ee3c th\u1eed nghi\u1ec7m v\u1edbi c\u00e1c v\u00ed d\u1ee5 m\u1edbi.<\/li>\n<li><strong>S\u1eed d\u1ee5ng b\u1ed9 h\u1ed7 tr\u1ee3<\/strong>: B\u1ed9 h\u1ed7 tr\u1ee3 ch\u1ee9a c\u00e1c v\u00ed d\u1ee5 v\u1ec1 c\u00e1c l\u1edbp \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 tham kh\u1ea3o.<\/li>\n<li><strong>So s\u00e1nh v\u00e0 ph\u00e2n lo\u1ea1i<\/strong>: M\u00f4 h\u00ecnh so s\u00e1nh v\u00ed d\u1ee5 m\u1edbi v\u1edbi b\u1ed9 h\u1ed7 tr\u1ee3 \u0111\u1ec3 ph\u00e2n lo\u1ea1i \u0111\u00fang.<\/li>\n<\/ol>\n<h2>Ph\u00e2n t\u00edch c\u00e1c \u0111\u1eb7c \u0111i\u1ec3m ch\u00ednh c\u1ee7a ph\u01b0\u01a1ng ph\u00e1p h\u1ecdc m\u1ed9t l\u1ea7n<\/h2>\n<ul>\n<li><strong>Hi\u1ec7u qu\u1ea3 d\u1eef li\u1ec7u<\/strong>: C\u1ea7n \u00edt d\u1eef li\u1ec7u h\u01a1n \u0111\u1ec3 hu\u1ea5n luy\u1ec7n.<\/li>\n<li><strong>Uy\u1ec3n chuy\u1ec3n<\/strong>: C\u00f3 th\u1ec3 \u00e1p d\u1ee5ng cho c\u00e1c nhi\u1ec7m v\u1ee5 m\u1edbi, ch\u01b0a \u0111\u01b0\u1ee3c nh\u00ecn th\u1ea5y.<\/li>\n<li><strong>Th\u00e1ch th\u1ee9c<\/strong>: Nh\u1ea1y c\u1ea3m v\u1edbi vi\u1ec7c trang b\u1ecb qu\u00e1 m\u1ee9c v\u00e0 y\u00eau c\u1ea7u tinh ch\u1ec9nh.<\/li>\n<\/ul>\n<h2>C\u00e1c lo\u1ea1i h\u00ecnh h\u1ecdc t\u1eadp m\u1ed9t l\u1ea7n<\/h2>\n<h3>B\u1ea3ng: C\u00e1c c\u00e1ch ti\u1ebfp c\u1eadn kh\u00e1c nhau<\/h3>\n<table>\n<thead>\n<tr>\n<th>Ti\u1ebfp c\u1eadn<\/th>\n<th>S\u1ef1 mi\u00eau t\u1ea3<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>M\u1ea1ng Xi\u00eam<\/td>\n<td>S\u1eed d\u1ee5ng m\u1ea1ng \u0111\u00f4i \u0111\u1ec3 h\u1ecdc t\u1eadp t\u01b0\u01a1ng t\u1ef1.<\/td>\n<\/tr>\n<tr>\n<td>M\u1ea1ng ph\u00f9 h\u1ee3p<\/td>\n<td>S\u1eed d\u1ee5ng c\u01a1 ch\u1ebf ch\u00fa \u00fd \u0111\u1ec3 ph\u00e2n lo\u1ea1i.<\/td>\n<\/tr>\n<tr>\n<td>M\u1ea1ng nguy\u00ean m\u1eabu<\/td>\n<td>T\u00ednh to\u00e1n nguy\u00ean m\u1eabu \u0111\u1ec3 ph\u00e2n lo\u1ea1i.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1ch s\u1eed d\u1ee5ng ph\u01b0\u01a1ng ph\u00e1p h\u1ecdc m\u1ed9t l\u1ea7n, c\u00e1c v\u1ea5n \u0111\u1ec1 v\u00e0 gi\u1ea3i ph\u00e1p c\u1ee7a ch\u00fang<\/h2>\n<h3>C\u00e1c \u1ee9ng d\u1ee5ng<\/h3>\n<ul>\n<li><strong>Nh\u1eadn d\u1ea1ng h\u00ecnh \u1ea3nh<\/strong><\/li>\n<li><strong>Nh\u1eadn d\u1ea1ng gi\u1ecdng n\u00f3i<\/strong><\/li>\n<li><strong>Ph\u00e1t hi\u1ec7n b\u1ea5t th\u01b0\u1eddng<\/strong><\/li>\n<\/ul>\n<h3>C\u00e1c v\u1ea5n \u0111\u1ec1<\/h3>\n<ul>\n<li><strong>Trang b\u1ecb qu\u00e1 m\u1ee9c<\/strong>: C\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c gi\u1ea3i quy\u1ebft b\u1eb1ng c\u00e1ch s\u1eed d\u1ee5ng c\u00e1c k\u1ef9 thu\u1eadt ch\u00ednh quy th\u00edch h\u1ee3p.<\/li>\n<li><strong>\u0110\u1ed9 nh\u1ea1y d\u1eef li\u1ec7u<\/strong>: Gi\u1ea3i quy\u1ebft b\u1eb1ng c\u00e1ch x\u1eed l\u00fd tr\u01b0\u1edbc d\u1eef li\u1ec7u c\u1ea9n th\u1eadn.<\/li>\n<\/ul>\n<h2>C\u00e1c \u0111\u1eb7c \u0111i\u1ec3m ch\u00ednh v\u00e0 nh\u1eefng so s\u00e1nh kh\u00e1c v\u1edbi c\u00e1c thu\u1eadt ng\u1eef t\u01b0\u01a1ng t\u1ef1<\/h2>\n<h3>B\u1ea3ng: So s\u00e1nh v\u1edbi H\u1ecdc nhi\u1ec1u l\u1ea7n<\/h3>\n<table>\n<thead>\n<tr>\n<th>T\u00ednh n\u0103ng<\/th>\n<th>H\u1ecdc m\u1ed9t l\u1ea7n<\/th>\n<th>H\u1ecdc nhi\u1ec1u l\u1ea7n<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Y\u00eau c\u1ea7u d\u1eef li\u1ec7u<\/td>\n<td>V\u00ed d\u1ee5 duy nh\u1ea5t cho m\u1ed7i l\u1edbp<\/td>\n<td>Nhi\u1ec1u v\u00ed d\u1ee5<\/td>\n<\/tr>\n<tr>\n<td>\u0110\u1ed9 ph\u1ee9c t\u1ea1p<\/td>\n<td>Cao h\u01a1n<\/td>\n<td>Th\u1ea5p h\u01a1n<\/td>\n<\/tr>\n<tr>\n<td>Kh\u1ea3 n\u0103ng \u1ee9ng d\u1ee5ng<\/td>\n<td>Nhi\u1ec7m v\u1ee5 c\u1ee5 th\u1ec3<\/td>\n<td>T\u1ed5ng quan<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 c\u1ee7a t\u01b0\u01a1ng lai li\u00ean quan \u0111\u1ebfn h\u1ecdc t\u1eadp m\u1ed9t l\u1ea7n<\/h2>\n<p>V\u1edbi s\u1ef1 ph\u00e1t tri\u1ec3n c\u1ee7a \u0111i\u1ec7n to\u00e1n bi\u00ean v\u00e0 c\u00e1c thi\u1ebft b\u1ecb IoT, ph\u01b0\u01a1ng ph\u00e1p h\u1ecdc m\u1ed9t l\u1ea7n s\u1ebd c\u00f3 m\u1ed9t t\u01b0\u01a1ng lai \u0111\u1ea7y h\u1ee9a h\u1eb9n. Nh\u1eefng c\u1ea3i ti\u1ebfn nh\u01b0 H\u1ecdc t\u1eadp \u00edt l\u1ea7n s\u1ebd m\u1edf r\u1ed9ng kh\u1ea3 n\u0103ng h\u01a1n n\u1eefa, d\u1ef1 ki\u1ebfn s\u1ebd ti\u1ebfp t\u1ee5c nghi\u00ean c\u1ee9u v\u00e0 ph\u00e1t tri\u1ec3n trong nh\u1eefng n\u0103m t\u1edbi.<\/p>\n<h2>C\u00e1ch s\u1eed d\u1ee5ng ho\u1eb7c li\u00ean k\u1ebft m\u00e1y ch\u1ee7 proxy v\u1edbi ph\u01b0\u01a1ng ph\u00e1p h\u1ecdc m\u1ed9t l\u1ea7n<\/h2>\n<p>C\u00e1c m\u00e1y ch\u1ee7 proxy gi\u1ed1ng nh\u01b0 c\u00e1c m\u00e1y ch\u1ee7 do OneProxy cung c\u1ea5p c\u00f3 th\u1ec3 \u0111\u00f3ng vai tr\u00f2 trong vi\u1ec7c h\u1ecdc m\u1ed9t l\u1ea7n b\u1eb1ng c\u00e1ch t\u1ea1o \u0111i\u1ec1u ki\u1ec7n truy\u1ec1n d\u1eef li\u1ec7u an to\u00e0n v\u00e0 hi\u1ec7u qu\u1ea3. Trong c\u00e1c t\u00ecnh hu\u1ed1ng nh\u01b0 ph\u00e1t hi\u1ec7n b\u1ea5t th\u01b0\u1eddng, thu\u1eadt to\u00e1n h\u1ecdc m\u1ed9t l\u1ea7n c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng c\u00f9ng v\u1edbi m\u00e1y ch\u1ee7 proxy \u0111\u1ec3 x\u00e1c \u0111\u1ecbnh c\u00e1c m\u1eabu \u0111\u1ed9c h\u1ea1i t\u1eeb l\u01b0\u1ee3ng d\u1eef li\u1ec7u t\u1ed1i thi\u1ec3u.<\/p>\n<h2>Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.cv-foundation.org\/openaccess\/content_cvpr_2005\/papers\/Fei-Fei_A_Bayesian_Hierarchical_2005_CVPR_paper.pdf\" target=\"_new\" rel=\"noopener nofollow\">M\u00f4 h\u00ecnh ph\u00e2n c\u1ea5p Bayesian \u0111\u1ec3 h\u1ecdc c\u00e1c h\u1ea1ng m\u1ee5c c\u1ea3nh t\u1ef1 nhi\u00ean<\/a><\/li>\n<li><a href=\"https:\/\/www.cs.cmu.edu\/~rsalakhu\/papers\/oneshot1.pdf\" target=\"_new\" rel=\"noopener nofollow\">M\u1ea1ng th\u1ea7n kinh Xi\u00eam \u0111\u1ec3 nh\u1eadn d\u1ea1ng h\u00ecnh \u1ea3nh m\u1ed9t l\u1ea7n<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/vn\/\" target=\"_new\" rel=\"noopener\">OneProxy<\/a>: \u0110\u1ec3 kh\u00e1m ph\u00e1 c\u00e1ch t\u00edch h\u1ee3p m\u00e1y ch\u1ee7 proxy v\u1edbi ph\u01b0\u01a1ng ph\u00e1p h\u1ecdc m\u1ed9t l\u1ea7n.<\/li>\n<\/ul>","protected":false},"featured_media":469058,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478261","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>One-shot Learning<\/mark>","faq_items":[{"question":"What is One-shot Learning?","answer":"<p>One-shot Learning is a classification task where a model learns to recognize objects, patterns, or subjects from a single example or \"one shot.\" Unlike conventional machine learning methods, it does not require extensive data for training and has applications in areas like computer vision, speech recognition, and natural language processing.<\/p>"},{"question":"What is the history of the origin of One-shot Learning?","answer":"<p>The concept of One-shot Learning was introduced in computer science in the early 2000s, reflecting human learning from single examples. A significant step was taken in 2005 with the publication of a paper by Li Fei-Fei, Rob Fergus, and Pietro Perona, leading to its integration in various AI applications.<\/p>"},{"question":"How does One-shot Learning work?","answer":"<p>One-shot Learning works by training the model with a small dataset, testing it with new examples, utilizing a support set for reference, and comparing and classifying the new examples accordingly. Approaches like Memory-Augmented Neural Networks (MANNs) and Meta-Learning are often employed.<\/p>"},{"question":"What are the key features of One-shot Learning?","answer":"<p>The key features of One-shot Learning include data efficiency as it requires fewer data for training, flexibility in applying to new, unseen tasks, and challenges like sensitivity to overfitting.<\/p>"},{"question":"What types of One-shot Learning exist?","answer":"<p>Types of One-shot Learning include Siamese Networks, which use twin networks for similarity learning; Matching Networks, utilizing attention mechanisms; and Prototypical Networks, calculating prototypes for classification.<\/p>"},{"question":"What are the ways to use One-shot Learning and what problems might arise?","answer":"<p>One-shot Learning is used in image recognition, speech recognition, and anomaly detection. Problems such as overfitting and data sensitivity can arise, which can be addressed by proper regularization techniques and careful data preprocessing.<\/p>"},{"question":"How is One-shot Learning compared to similar terms like Multi-shot Learning?","answer":"<p>One-shot Learning requires a single example per class, has higher complexity, and is applicable to specific tasks. In contrast, Multi-shot Learning needs multiple examples, has lower complexity, and is generally applicable.<\/p>"},{"question":"What are the future perspectives related to One-shot Learning?","answer":"<p>The future of One-shot Learning is promising, with potential growth in edge computing and IoT devices. Enhancements like Few-Shot Learning expand the capabilities further, and continuous research is expected.<\/p>"},{"question":"How can proxy servers like OneProxy be associated with One-shot Learning?","answer":"<p>Proxy servers like OneProxy can be associated with One-shot Learning by facilitating secure and efficient data transmission. They can also be used in conjunction with one-shot learning for tasks like anomaly detection to identify malicious patterns from minimal data.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/478261","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/478261\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/469058"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=478261"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}