{"id":475945,"date":"2023-08-09T07:24:43","date_gmt":"2023-08-09T07:24:43","guid":{"rendered":""},"modified":"2023-09-05T11:11:40","modified_gmt":"2023-09-05T11:11:40","slug":"autoencoders","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/autoencoders\/","title":{"rendered":"B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng"},"content":{"rendered":"<p>B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng l\u00e0 m\u1ed9t l\u1edbp m\u1ea1ng th\u1ea7n kinh nh\u00e2n t\u1ea1o thi\u1ebft y\u1ebfu v\u00e0 linh ho\u1ea1t, ch\u1ee7 y\u1ebfu \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng cho c\u00e1c nhi\u1ec7m v\u1ee5 h\u1ecdc t\u1eadp kh\u00f4ng gi\u00e1m s\u00e1t. Ch\u00fang \u0111\u00e1ng ch\u00fa \u00fd v\u00ec kh\u1ea3 n\u0103ng th\u1ef1c hi\u1ec7n c\u00e1c nhi\u1ec7m v\u1ee5 nh\u01b0 gi\u1ea3m k\u00edch th\u01b0\u1edbc, h\u1ecdc t\u00ednh n\u0103ng v\u00e0 th\u1eadm ch\u00ed l\u00e0 t\u1ea1o m\u00f4 h\u00ecnh t\u1ed5ng qu\u00e1t.<\/p>\n<h2>L\u1ecbch s\u1eed c\u1ee7a b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng<\/h2>\n<p>Kh\u00e1i ni\u1ec7m v\u1ec1 b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng b\u1eaft ngu\u1ed3n t\u1eeb nh\u1eefng n\u0103m 1980 v\u1edbi s\u1ef1 ph\u00e1t tri\u1ec3n c\u1ee7a M\u1ea1ng Hopfield, ti\u1ec1n th\u00e2n c\u1ee7a b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng hi\u1ec7n \u0111\u1ea1i. C\u00f4ng tr\u00ecnh \u0111\u1ea7u ti\u00ean \u0111\u1ec1 xu\u1ea5t \u00fd t\u01b0\u1edfng v\u1ec1 b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng l\u00e0 c\u1ee7a Rumelhart v\u00e0 c\u1ed9ng s\u1ef1 v\u00e0o n\u0103m 1986, trong nh\u1eefng ng\u00e0y \u0111\u1ea7u c\u1ee7a m\u1ea1ng l\u01b0\u1edbi th\u1ea7n kinh nh\u00e2n t\u1ea1o. Thu\u1eadt ng\u1eef &#039;b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng&#039; \u0111\u01b0\u1ee3c h\u00ecnh th\u00e0nh sau \u0111\u00f3, khi c\u00e1c nh\u00e0 khoa h\u1ecdc b\u1eaft \u0111\u1ea7u nh\u1eadn ra kh\u1ea3 n\u0103ng t\u1ef1 m\u00e3 h\u00f3a \u0111\u1ed9c \u0111\u00e1o c\u1ee7a ch\u00fang. Trong nh\u1eefng n\u0103m g\u1ea7n \u0111\u00e2y, v\u1edbi s\u1ef1 ph\u00e1t tri\u1ec3n c\u1ee7a deep learning, b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng \u0111\u00e3 tr\u1ea3i qua th\u1eddi k\u1ef3 ph\u1ee5c h\u01b0ng, \u0111\u00f3ng g\u00f3p \u0111\u00e1ng k\u1ec3 v\u00e0o c\u00e1c l\u0129nh v\u1ef1c nh\u01b0 ph\u00e1t hi\u1ec7n b\u1ea5t th\u01b0\u1eddng, gi\u1ea3m ti\u1ebfng \u1ed3n v\u00e0 th\u1eadm ch\u00ed c\u1ea3 c\u00e1c m\u00f4 h\u00ecnh t\u1ed5ng h\u1ee3p nh\u01b0 B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng bi\u1ebfn thi\u00ean (VAE).<\/p>\n<h2>Kh\u00e1m ph\u00e1 b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng<\/h2>\n<p>B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng l\u00e0 m\u1ed9t lo\u1ea1i m\u1ea1ng th\u1ea7n kinh nh\u00e2n t\u1ea1o \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 t\u00ecm hi\u1ec3u c\u00e1ch m\u00e3 h\u00f3a d\u1eef li\u1ec7u \u0111\u1ea7u v\u00e0o m\u1ed9t c\u00e1ch hi\u1ec7u qu\u1ea3. \u00dd t\u01b0\u1edfng trung t\u00e2m l\u00e0 m\u00e3 h\u00f3a \u0111\u1ea7u v\u00e0o th\u00e0nh bi\u1ec3u di\u1ec5n n\u00e9n v\u00e0 sau \u0111\u00f3 x\u00e2y d\u1ef1ng l\u1ea1i \u0111\u1ea7u v\u00e0o ban \u0111\u1ea7u t\u1eeb bi\u1ec3u di\u1ec5n n\u00e0y m\u1ed9t c\u00e1ch ch\u00ednh x\u00e1c nh\u1ea5t c\u00f3 th\u1ec3. Qu\u00e1 tr\u00ecnh n\u00e0y bao g\u1ed3m hai th\u00e0nh ph\u1ea7n ch\u00ednh: b\u1ed9 m\u00e3 h\u00f3a, chuy\u1ec3n \u0111\u1ed5i d\u1eef li\u1ec7u \u0111\u1ea7u v\u00e0o th\u00e0nh m\u00e3 nh\u1ecf g\u1ecdn v\u00e0 b\u1ed9 gi\u1ea3i m\u00e3, t\u00e1i t\u1ea1o l\u1ea1i \u0111\u1ea7u v\u00e0o ban \u0111\u1ea7u t\u1eeb m\u00e3.<\/p>\n<p>M\u1ee5c ti\u00eau c\u1ee7a b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng l\u00e0 gi\u1ea3m thi\u1ec3u s\u1ef1 kh\u00e1c bi\u1ec7t (ho\u1eb7c l\u1ed7i) gi\u1eefa \u0111\u1ea7u v\u00e0o ban \u0111\u1ea7u v\u00e0 \u0111\u1ea7u ra \u0111\u01b0\u1ee3c t\u00e1i t\u1ea1o, t\u1eeb \u0111\u00f3 t\u00ecm hi\u1ec3u c\u00e1c t\u00ednh n\u0103ng c\u1ea7n thi\u1ebft nh\u1ea5t trong d\u1eef li\u1ec7u. M\u00e3 n\u00e9n m\u00e0 b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng h\u1ecdc th\u01b0\u1eddng c\u00f3 s\u1ed1 chi\u1ec1u th\u1ea5p h\u01a1n nhi\u1ec1u so v\u1edbi d\u1eef li\u1ec7u g\u1ed1c, d\u1eabn \u0111\u1ebfn vi\u1ec7c b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng r\u1ed9ng r\u00e3i trong c\u00e1c nhi\u1ec7m v\u1ee5 gi\u1ea3m k\u00edch th\u01b0\u1edbc.<\/p>\n<h2>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng<\/h2>\n<p>Ki\u1ebfn tr\u00fac c\u1ee7a b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng bao g\u1ed3m ba ph\u1ea7n ch\u00ednh:<\/p>\n<ol>\n<li>\n<p><strong>M\u00e3 ho\u00e1:<\/strong> Ph\u1ea7n m\u1ea1ng n\u00e0y n\u00e9n \u0111\u1ea7u v\u00e0o th\u00e0nh bi\u1ec3u di\u1ec5n kh\u00f4ng gian ti\u1ec1m \u1ea9n. N\u00f3 m\u00e3 h\u00f3a h\u00ecnh \u1ea3nh \u0111\u1ea7u v\u00e0o d\u01b0\u1edbi d\u1ea1ng bi\u1ec3u di\u1ec5n n\u00e9n \u1edf k\u00edch th\u01b0\u1edbc thu nh\u1ecf. Th\u00f4ng th\u01b0\u1eddng, h\u00ecnh \u1ea3nh n\u00e9n ch\u1ee9a th\u00f4ng tin quan tr\u1ecdng v\u1ec1 h\u00ecnh \u1ea3nh \u0111\u1ea7u v\u00e0o.<\/p>\n<\/li>\n<li>\n<p><strong>N\u00fat c\u1ed5 chai:<\/strong> L\u1edbp n\u00e0y n\u1eb1m gi\u1eefa b\u1ed9 m\u00e3 h\u00f3a v\u00e0 b\u1ed9 gi\u1ea3i m\u00e3. N\u00f3 ch\u1ee9a bi\u1ec3u di\u1ec5n n\u00e9n c\u1ee7a d\u1eef li\u1ec7u \u0111\u1ea7u v\u00e0o. \u0110\u00e2y l\u00e0 k\u00edch th\u01b0\u1edbc th\u1ea5p nh\u1ea5t c\u00f3 th\u1ec3 c\u00f3 c\u1ee7a d\u1eef li\u1ec7u \u0111\u1ea7u v\u00e0o.<\/p>\n<\/li>\n<li>\n<p><strong>B\u1ed9 gi\u1ea3i m\u00e3:<\/strong> Ph\u1ea7n m\u1ea1ng n\u00e0y t\u00e1i t\u1ea1o l\u1ea1i h\u00ecnh \u1ea3nh \u0111\u1ea7u v\u00e0o t\u1eeb d\u1ea1ng \u0111\u01b0\u1ee3c m\u00e3 h\u00f3a c\u1ee7a n\u00f3. Qu\u00e1 tr\u00ecnh t\u00e1i t\u1ea1o s\u1ebd l\u00e0 s\u1ef1 t\u00e1i t\u1ea1o b\u1ecb m\u1ea5t m\u00e1t c\u1ee7a \u0111\u1ea7u v\u00e0o ban \u0111\u1ea7u, \u0111\u1eb7c bi\u1ec7t n\u1ebfu k\u00edch th\u01b0\u1edbc m\u00e3 h\u00f3a nh\u1ecf h\u01a1n k\u00edch th\u01b0\u1edbc \u0111\u1ea7u v\u00e0o.<\/p>\n<\/li>\n<\/ol>\n<p>M\u1ed7i ph\u1ea7n n\u00e0y bao g\u1ed3m nhi\u1ec1u l\u1edbp n\u01a1-ron v\u00e0 ki\u1ebfn tr\u00fac c\u1ee5 th\u1ec3 (s\u1ed1 l\u1edbp, s\u1ed1 l\u01b0\u1ee3ng n\u01a1-ron tr\u00ean m\u1ed7i l\u1edbp, v.v.) c\u00f3 th\u1ec3 kh\u00e1c nhau t\u00f9y thu\u1ed9c v\u00e0o \u1ee9ng d\u1ee5ng.<\/p>\n<h2>C\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng<\/h2>\n<ul>\n<li>\n<p><strong>D\u1eef li\u1ec7u c\u1ee5 th\u1ec3:<\/strong> B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf d\u00e0nh ri\u00eang cho d\u1eef li\u1ec7u, ngh\u0129a l\u00e0 ch\u00fang s\u1ebd kh\u00f4ng m\u00e3 h\u00f3a d\u1eef li\u1ec7u m\u00e0 ch\u00fang ch\u01b0a \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o.<\/p>\n<\/li>\n<li>\n<p><strong>M\u1ea5t m\u00e1t:<\/strong> Vi\u1ec7c x\u00e2y d\u1ef1ng l\u1ea1i d\u1eef li\u1ec7u \u0111\u1ea7u v\u00e0o s\u1ebd b\u1ecb \u201cm\u1ea5t m\u00e1t\u201d, h\u00e0m \u00fd m\u1ed9t s\u1ed1 th\u00f4ng tin lu\u00f4n b\u1ecb m\u1ea5t trong qu\u00e1 tr\u00ecnh m\u00e3 h\u00f3a.<\/p>\n<\/li>\n<li>\n<p><strong>Kh\u00f4ng \u0111\u01b0\u1ee3c gi\u00e1m s\u00e1t:<\/strong> B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng l\u00e0 m\u1ed9t k\u1ef9 thu\u1eadt h\u1ecdc kh\u00f4ng gi\u00e1m s\u00e1t v\u00ec ch\u00fang kh\u00f4ng y\u00eau c\u1ea7u nh\u00e3n r\u00f5 r\u00e0ng \u0111\u1ec3 h\u1ecdc c\u00e1ch bi\u1ec3u di\u1ec5n.<\/p>\n<\/li>\n<li>\n<p><strong>Gi\u1ea3m k\u00edch th\u01b0\u1edbc:<\/strong> Ch\u00fang th\u01b0\u1eddng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 gi\u1ea3m k\u00edch th\u01b0\u1edbc, trong \u0111\u00f3 ch\u00fang c\u00f3 th\u1ec3 ho\u1ea1t \u0111\u1ed9ng t\u1ed1t h\u01a1n c\u00e1c k\u1ef9 thu\u1eadt nh\u01b0 PCA b\u1eb1ng c\u00e1ch h\u1ecdc c\u00e1c ph\u00e9p bi\u1ebfn \u0111\u1ed5i phi tuy\u1ebfn t\u00ednh.<\/p>\n<\/li>\n<\/ul>\n<h2>C\u00e1c lo\u1ea1i b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng<\/h2>\n<p>C\u00f3 m\u1ed9t s\u1ed1 lo\u1ea1i b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng, m\u1ed7i lo\u1ea1i c\u00f3 \u0111\u1eb7c \u0111i\u1ec3m v\u00e0 c\u00e1ch s\u1eed d\u1ee5ng ri\u00eang. D\u01b0\u1edbi \u0111\u00e2y l\u00e0 m\u1ed9t s\u1ed1 c\u00e1i ph\u1ed5 bi\u1ebfn:<\/p>\n<ol>\n<li>\n<p><strong>B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng Vanilla:<\/strong> H\u00ecnh th\u1ee9c \u0111\u01a1n gi\u1ea3n nh\u1ea5t c\u1ee7a b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng l\u00e0 m\u1ea1ng n\u01a1 ron kh\u00f4ng t\u00e1i di\u1ec5n, chuy\u1ec3n ti\u1ebfp t\u01b0\u01a1ng t\u1ef1 nh\u01b0 perceptron nhi\u1ec1u l\u1edbp.<\/p>\n<\/li>\n<li>\n<p><strong>B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng nhi\u1ec1u l\u1edbp:<\/strong> N\u1ebfu b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng s\u1eed d\u1ee5ng nhi\u1ec1u l\u1edbp \u1ea9n cho qu\u00e1 tr\u00ecnh m\u00e3 h\u00f3a v\u00e0 gi\u1ea3i m\u00e3 th\u00ec n\u00f3 \u0111\u01b0\u1ee3c coi l\u00e0 B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng nhi\u1ec1u l\u1edbp.<\/p>\n<\/li>\n<li>\n<p><strong>B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng t\u00edch ch\u1eadp:<\/strong> C\u00e1c b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng n\u00e0y s\u1eed d\u1ee5ng c\u00e1c l\u1edbp t\u00edch ch\u1eadp thay v\u00ec c\u00e1c l\u1edbp \u0111\u01b0\u1ee3c k\u1ebft n\u1ed1i \u0111\u1ea7y \u0111\u1ee7 v\u00e0 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng v\u1edbi d\u1eef li\u1ec7u h\u00ecnh \u1ea3nh.<\/p>\n<\/li>\n<li>\n<p><strong>B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng th\u01b0a th\u1edbt:<\/strong> C\u00e1c b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng n\u00e0y \u00e1p \u0111\u1eb7t \u0111\u1ed9 th\u01b0a th\u1edbt l\u00ean c\u00e1c \u0111\u01a1n v\u1ecb \u1ea9n trong qu\u00e1 tr\u00ecnh \u0111\u00e0o t\u1ea1o \u0111\u1ec3 t\u00ecm hi\u1ec3u c\u00e1c t\u00ednh n\u0103ng m\u1ea1nh m\u1ebd h\u01a1n.<\/p>\n<\/li>\n<li>\n<p><strong>B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng kh\u1eed nhi\u1ec5u:<\/strong> C\u00e1c b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng n\u00e0y \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o \u0111\u1ec3 t\u00e1i t\u1ea1o l\u1ea1i \u0111\u1ea7u v\u00e0o t\u1eeb phi\u00ean b\u1ea3n b\u1ecb l\u1ed7i c\u1ee7a n\u00f3, gi\u00fap gi\u1ea3m ti\u1ebfng \u1ed3n.<\/p>\n<\/li>\n<li>\n<p><strong>B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng bi\u1ebfn \u0111\u1ed5i (VAE):<\/strong> VAE l\u00e0 m\u1ed9t lo\u1ea1i b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng t\u1ea1o ra kh\u00f4ng gian ti\u1ec1m \u1ea9n c\u00f3 c\u1ea5u tr\u00fac li\u00ean t\u1ee5c, r\u1ea5t h\u1eefu \u00edch cho m\u00f4 h\u00ecnh t\u1ed5ng qu\u00e1t.<\/p>\n<\/li>\n<\/ol>\n<table>\n<thead>\n<tr>\n<th>Lo\u1ea1i b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng<\/th>\n<th>\u0110\u1eb7c tr\u01b0ng<\/th>\n<th>C\u00e1c tr\u01b0\u1eddng h\u1ee3p s\u1eed d\u1ee5ng \u0111i\u1ec3n h\u00ecnh<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Vanilla<\/td>\n<td>D\u1ea1ng \u0111\u01a1n gi\u1ea3n nh\u1ea5t, t\u01b0\u01a1ng t\u1ef1 nh\u01b0 perceptron nhi\u1ec1u l\u1edbp<\/td>\n<td>Gi\u1ea3m k\u00edch th\u01b0\u1edbc c\u01a1 b\u1ea3n<\/td>\n<\/tr>\n<tr>\n<td>Nhi\u1ec1u l\u1edbp<\/td>\n<td>Nhi\u1ec1u l\u1edbp \u1ea9n \u0111\u1ec3 m\u00e3 h\u00f3a v\u00e0 gi\u1ea3i m\u00e3<\/td>\n<td>Gi\u1ea3m k\u00edch th\u01b0\u1edbc ph\u1ee9c t\u1ea1p<\/td>\n<\/tr>\n<tr>\n<td>t\u00edch ch\u1eadp<\/td>\n<td>S\u1eed d\u1ee5ng c\u00e1c l\u1edbp ch\u1eadp, th\u01b0\u1eddng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng v\u1edbi d\u1eef li\u1ec7u h\u00ecnh \u1ea3nh<\/td>\n<td>Nh\u1eadn d\u1ea1ng h\u00ecnh \u1ea3nh, Gi\u1ea3m nhi\u1ec5u h\u00ecnh \u1ea3nh<\/td>\n<\/tr>\n<tr>\n<td>th\u01b0a th\u1edbt<\/td>\n<td>\u00c1p \u0111\u1eb7t s\u1ef1 th\u01b0a th\u1edbt tr\u00ean c\u00e1c \u0111\u01a1n v\u1ecb \u1ea9n<\/td>\n<td>L\u1ef1a ch\u1ecdn t\u00ednh n\u0103ng<\/td>\n<\/tr>\n<tr>\n<td>Gi\u1ea3m nhi\u1ec5u<\/td>\n<td>\u0110\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o \u0111\u1ec3 x\u00e2y d\u1ef1ng l\u1ea1i \u0111\u1ea7u v\u00e0o t\u1eeb m\u1ed9t phi\u00ean b\u1ea3n b\u1ecb h\u1ecfng<\/td>\n<td>Gi\u1ea3m ti\u1ebfng \u1ed3n<\/td>\n<\/tr>\n<tr>\n<td>bi\u1ebfn th\u1ec3<\/td>\n<td>T\u1ea1o ra m\u1ed9t kh\u00f4ng gian ti\u1ec1m \u1ea9n c\u00f3 c\u1ea5u tr\u00fac li\u00ean t\u1ee5c<\/td>\n<td>M\u00f4 h\u00ecnh s\u00e1ng t\u1ea1o<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>S\u1eed d\u1ee5ng b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng: \u1ee8ng d\u1ee5ng v\u00e0 th\u00e1ch th\u1ee9c<\/h2>\n<p>B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng c\u00f3 nhi\u1ec1u \u1ee9ng d\u1ee5ng trong h\u1ecdc m\u00e1y v\u00e0 ph\u00e2n t\u00edch d\u1eef li\u1ec7u:<\/p>\n<ol>\n<li>\n<p><strong>N\u00e9n d\u1eef li\u1ec7u:<\/strong> B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o \u0111\u1ec3 n\u00e9n d\u1eef li\u1ec7u theo c\u00e1ch c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c t\u00e1i t\u1ea1o l\u1ea1i m\u1ed9t c\u00e1ch ho\u00e0n h\u1ea3o.<\/p>\n<\/li>\n<li>\n<p><strong>M\u00e0u s\u1eafc h\u00ecnh \u1ea3nh:<\/strong> B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 chuy\u1ec3n \u0111\u1ed5i h\u00ecnh \u1ea3nh \u0111en tr\u1eafng sang m\u00e0u.<\/p>\n<\/li>\n<li>\n<p><strong>Ph\u00e1t hi\u1ec7n b\u1ea5t th\u01b0\u1eddng:<\/strong> B\u1eb1ng c\u00e1ch \u0111\u00e0o t\u1ea1o tr\u00ean d\u1eef li\u1ec7u &#039;b\u00ecnh th\u01b0\u1eddng&#039;, b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 ph\u00e1t hi\u1ec7n c\u00e1c \u0111i\u1ec3m b\u1ea5t th\u01b0\u1eddng b\u1eb1ng c\u00e1ch so s\u00e1nh l\u1ed7i t\u00e1i t\u1ea1o.<\/p>\n<\/li>\n<li>\n<p><strong>H\u00ecnh \u1ea3nh kh\u1eed nhi\u1ec5u:<\/strong> B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 lo\u1ea1i b\u1ecf nhi\u1ec5u kh\u1ecfi h\u00ecnh \u1ea3nh, m\u1ed9t qu\u00e1 tr\u00ecnh \u0111\u01b0\u1ee3c g\u1ecdi l\u00e0 kh\u1eed nhi\u1ec5u.<\/p>\n<\/li>\n<li>\n<p><strong>T\u1ea1o d\u1eef li\u1ec7u m\u1edbi:<\/strong> B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng bi\u1ebfn th\u1ec3 c\u00f3 th\u1ec3 t\u1ea1o ra d\u1eef li\u1ec7u m\u1edbi c\u00f3 c\u00f9ng s\u1ed1 li\u1ec7u th\u1ed1ng k\u00ea v\u1edbi d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n.<\/p>\n<\/li>\n<\/ol>\n<p>Tuy nhi\u00ean, b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng c\u0169ng c\u00f3 th\u1ec3 \u0111\u1eb7t ra nh\u1eefng th\u00e1ch th\u1ee9c:<\/p>\n<ul>\n<li>\n<p>B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng c\u00f3 th\u1ec3 nh\u1ea1y c\u1ea3m v\u1edbi thang \u0111o d\u1eef li\u1ec7u \u0111\u1ea7u v\u00e0o. Vi\u1ec7c chia t\u1ef7 l\u1ec7 t\u00ednh n\u0103ng th\u01b0\u1eddng c\u1ea7n thi\u1ebft \u0111\u1ec3 c\u00f3 \u0111\u01b0\u1ee3c k\u1ebft qu\u1ea3 t\u1ed1t.<\/p>\n<\/li>\n<li>\n<p>Ki\u1ebfn tr\u00fac l\u00fd t\u01b0\u1edfng (t\u1ee9c l\u00e0 s\u1ed1 l\u01b0\u1ee3ng l\u1edbp v\u00e0 s\u1ed1 l\u01b0\u1ee3ng n\u00fat tr\u00ean m\u1ed7i l\u1edbp) r\u1ea5t c\u1ee5 th\u1ec3 cho v\u1ea5n \u0111\u1ec1 v\u00e0 th\u01b0\u1eddng y\u00eau c\u1ea7u th\u1eed nghi\u1ec7m r\u1ed9ng r\u00e3i.<\/p>\n<\/li>\n<li>\n<p>Bi\u1ec3u di\u1ec5n n\u00e9n thu \u0111\u01b0\u1ee3c th\u01b0\u1eddng kh\u00f4ng d\u1ec5 hi\u1ec3u, kh\u00f4ng gi\u1ed1ng nh\u01b0 c\u00e1c k\u1ef9 thu\u1eadt nh\u01b0 PCA.<\/p>\n<\/li>\n<li>\n<p>B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng c\u00f3 th\u1ec3 nh\u1ea1y c\u1ea3m v\u1edbi t\u00ecnh tr\u1ea1ng trang b\u1ecb qu\u00e1 m\u1ee9c, \u0111\u1eb7c bi\u1ec7t khi ki\u1ebfn tr\u00fac m\u1ea1ng c\u00f3 dung l\u01b0\u1ee3ng cao.<\/p>\n<\/li>\n<\/ul>\n<h2>So s\u00e1nh v\u00e0 c\u00e1c k\u1ef9 thu\u1eadt li\u00ean quan<\/h2>\n<p>B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c so s\u00e1nh v\u1edbi c\u00e1c k\u1ef9 thu\u1eadt gi\u1ea3m k\u00edch th\u01b0\u1edbc v\u00e0 h\u1ecdc kh\u00f4ng gi\u00e1m s\u00e1t kh\u00e1c, nh\u01b0 sau:<\/p>\n<table>\n<thead>\n<tr>\n<th>K\u1ef9 thu\u1eadt<\/th>\n<th>Kh\u00f4ng \u0111\u01b0\u1ee3c gi\u00e1m s\u00e1t<\/th>\n<th>Phi tuy\u1ebfn t\u00ednh<\/th>\n<th>L\u1ef1a ch\u1ecdn t\u00ednh n\u0103ng t\u00edch h\u1ee3p<\/th>\n<th>Kh\u1ea3 n\u0103ng s\u00e1ng t\u1ea1o<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng<\/td>\n<td>\u0110\u00fang<\/td>\n<td>\u0110\u00fang<\/td>\n<td>C\u00f3 (B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng th\u01b0a th\u1edbt)<\/td>\n<td>C\u00f3 (VAE)<\/td>\n<\/tr>\n<tr>\n<td>PCA<\/td>\n<td>\u0110\u00fang<\/td>\n<td>KH\u00d4NG<\/td>\n<td>KH\u00d4NG<\/td>\n<td>KH\u00d4NG<\/td>\n<\/tr>\n<tr>\n<td>t-SNE<\/td>\n<td>\u0110\u00fang<\/td>\n<td>\u0110\u00fang<\/td>\n<td>KH\u00d4NG<\/td>\n<td>KH\u00d4NG<\/td>\n<\/tr>\n<tr>\n<td>K-ngh\u0129a l\u00e0 ph\u00e2n c\u1ee5m<\/td>\n<td>\u0110\u00fang<\/td>\n<td>KH\u00d4NG<\/td>\n<td>KH\u00d4NG<\/td>\n<td>KH\u00d4NG<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Quan \u0111i\u1ec3m t\u01b0\u01a1ng lai v\u1ec1 b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng<\/h2>\n<p>B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng \u0111ang \u0111\u01b0\u1ee3c li\u00ean t\u1ee5c c\u1ea3i ti\u1ebfn v\u00e0 c\u1ea3i ti\u1ebfn. Trong t\u01b0\u01a1ng lai, b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng d\u1ef1 ki\u1ebfn s\u1ebd \u0111\u00f3ng m\u1ed9t vai tr\u00f2 l\u1edbn h\u01a1n n\u1eefa trong vi\u1ec7c h\u1ecdc t\u1eadp kh\u00f4ng gi\u00e1m s\u00e1t v\u00e0 b\u00e1n gi\u00e1m s\u00e1t, ph\u00e1t hi\u1ec7n b\u1ea5t th\u01b0\u1eddng v\u00e0 t\u1ea1o m\u00f4 h\u00ecnh t\u1ed5ng qu\u00e1t.<\/p>\n<p>M\u1ed9t \u0111i\u1ec3m th\u00fa v\u1ecb l\u00e0 s\u1ef1 k\u1ebft h\u1ee3p gi\u1eefa b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng v\u1edbi h\u1ecdc t\u0103ng c\u01b0\u1eddng (RL). B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng c\u00f3 th\u1ec3 gi\u00fap t\u00ecm hi\u1ec3u c\u00e1ch tr\u00ecnh b\u00e0y hi\u1ec7u qu\u1ea3 c\u1ee7a m\u1ed9t m\u00f4i tr\u01b0\u1eddng, l\u00e0m cho thu\u1eadt to\u00e1n RL hi\u1ec7u qu\u1ea3 h\u01a1n. Ngo\u00e0i ra, vi\u1ec7c t\u00edch h\u1ee3p b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng v\u1edbi c\u00e1c m\u00f4 h\u00ecnh t\u1ed5ng qu\u00e1t kh\u00e1c, nh\u01b0 M\u1ea1ng \u0111\u1ed1i th\u1ee7 s\u00e1ng t\u1ea1o (GAN), l\u00e0 m\u1ed9t con \u0111\u01b0\u1eddng \u0111\u1ea7y h\u1ee9a h\u1eb9n kh\u00e1c \u0111\u1ec3 t\u1ea1o ra c\u00e1c m\u00f4 h\u00ecnh t\u1ed5ng qu\u00e1t m\u1ea1nh m\u1ebd h\u01a1n.<\/p>\n<h2>B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng v\u00e0 m\u00e1y ch\u1ee7 proxy<\/h2>\n<p>M\u1ed1i quan h\u1ec7 gi\u1eefa b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng v\u00e0 m\u00e1y ch\u1ee7 proxy kh\u00f4ng tr\u1ef1c ti\u1ebfp m\u00e0 ch\u1ee7 y\u1ebfu l\u00e0 theo ng\u1eef c\u1ea3nh. M\u00e1y ch\u1ee7 proxy ch\u1ee7 y\u1ebfu ho\u1ea1t \u0111\u1ed9ng nh\u01b0 m\u1ed9t trung gian cho c\u00e1c y\u00eau c\u1ea7u t\u1eeb kh\u00e1ch h\u00e0ng \u0111ang t\u00ecm ki\u1ebfm t\u00e0i nguy\u00ean t\u1eeb c\u00e1c m\u00e1y ch\u1ee7 kh\u00e1c, cung c\u1ea5p nhi\u1ec1u ch\u1ee9c n\u0103ng kh\u00e1c nhau nh\u01b0 b\u1ea3o v\u1ec7 quy\u1ec1n ri\u00eang t\u01b0, ki\u1ec3m so\u00e1t truy c\u1eadp v\u00e0 b\u1ed9 nh\u1edb \u0111\u1ec7m.<\/p>\n<p>M\u1eb7c d\u00f9 vi\u1ec7c s\u1eed d\u1ee5ng b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng c\u00f3 th\u1ec3 kh\u00f4ng tr\u1ef1c ti\u1ebfp n\u00e2ng cao kh\u1ea3 n\u0103ng c\u1ee7a m\u00e1y ch\u1ee7 proxy nh\u01b0ng ch\u00fang c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c t\u1eadn d\u1ee5ng trong c\u00e1c h\u1ec7 th\u1ed1ng l\u1edbn h\u01a1n n\u01a1i m\u00e1y ch\u1ee7 proxy l\u00e0 m\u1ed9t ph\u1ea7n c\u1ee7a m\u1ea1ng. V\u00ed d\u1ee5: n\u1ebfu m\u00e1y ch\u1ee7 proxy l\u00e0 m\u1ed9t ph\u1ea7n c\u1ee7a h\u1ec7 th\u1ed1ng x\u1eed l\u00fd l\u01b0\u1ee3ng l\u1edbn d\u1eef li\u1ec7u, b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 n\u00e9n d\u1eef li\u1ec7u ho\u1eb7c ph\u00e1t hi\u1ec7n c\u00e1c \u0111i\u1ec3m b\u1ea5t th\u01b0\u1eddng trong l\u01b0u l\u01b0\u1ee3ng m\u1ea1ng.<\/p>\n<p>M\u1ed9t \u1ee9ng d\u1ee5ng ti\u1ec1m n\u0103ng kh\u00e1c l\u00e0 trong b\u1ed1i c\u1ea3nh VPN ho\u1eb7c c\u00e1c m\u00e1y ch\u1ee7 proxy b\u1ea3o m\u1eadt kh\u00e1c, n\u01a1i b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng l\u00e0m c\u01a1 ch\u1ebf ph\u00e1t hi\u1ec7n c\u00e1c m\u1eabu b\u1ea5t th\u01b0\u1eddng ho\u1eb7c b\u1ea5t th\u01b0\u1eddng trong l\u01b0u l\u01b0\u1ee3ng truy c\u1eadp m\u1ea1ng, g\u00f3p ph\u1ea7n t\u0103ng c\u01b0\u1eddng t\u00ednh b\u1ea3o m\u1eadt c\u1ee7a m\u1ea1ng.<\/p>\n<h2>Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n<p>\u0110\u1ec3 kh\u00e1m ph\u00e1 th\u00eam v\u1ec1 B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng, h\u00e3y tham kh\u1ea3o c\u00e1c t\u00e0i nguy\u00ean sau:<\/p>\n<ol>\n<li>\n<p><a href=\"https:\/\/www.deeplearningbook.org\/contents\/autoencoders.html\" target=\"_new\" rel=\"noopener nofollow\">B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng trong Deep Learning<\/a> \u2013 S\u00e1ch gi\u00e1o khoa Deep Learning c\u1ee7a Goodfellow, Bengio v\u00e0 Courville.<\/p>\n<\/li>\n<li>\n<p><a href=\"https:\/\/blog.keras.io\/building-autoencoders-in-keras.html\" target=\"_new\" rel=\"noopener nofollow\">X\u00e2y d\u1ef1ng b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng \u1edf Keras<\/a> \u2013 H\u01b0\u1edbng d\u1eabn tri\u1ec3n khai b\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng trong Keras.<\/p>\n<\/li>\n<li>\n<p><a href=\"https:\/\/wiseodd.github.io\/techblog\/2016\/12\/10\/variational-autoencoder\/\" target=\"_new\" rel=\"noopener nofollow\">B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng bi\u1ebfn th\u1ec3: Tr\u1ef1c gi\u00e1c v\u00e0 tri\u1ec3n khai<\/a> \u2013 Gi\u1ea3i th\u00edch v\u00e0 th\u1ef1c hi\u1ec7n B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng bi\u1ebfn thi\u00ean.<\/p>\n<\/li>\n<li>\n<p><a href=\"http:\/\/deeplearning.stanford.edu\/tutorial\/supervised\/FeatureExtractionUsingConvolution\/\" target=\"_new\" rel=\"noopener nofollow\">B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng th\u01b0a th\u1edbt<\/a> \u2013 H\u01b0\u1edbng d\u1eabn c\u1ee7a \u0110\u1ea1i h\u1ecdc Stanford v\u1ec1 B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng th\u01b0a th\u1edbt.<\/p>\n<\/li>\n<li>\n<p><a href=\"https:\/\/towardsdatascience.com\/understanding-variational-autoencoders-vaes-f70510919f73\" target=\"_new\" rel=\"noopener nofollow\">T\u00ecm hi\u1ec3u v\u1ec1 B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng bi\u1ebfn thi\u00ean (VAE)<\/a> \u2013 B\u00e0i vi\u1ebft t\u1ed5ng h\u1ee3p v\u1ec1 B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng bi\u1ebfn \u0111\u1ed5i t\u1eeb H\u01b0\u1edbng t\u1edbi Khoa h\u1ecdc D\u1eef li\u1ec7u.<\/p>\n<\/li>\n<\/ol>","protected":false},"featured_media":467668,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-475945","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Autoencoders: Unsupervised Learning and Data Compression<\/mark>","faq_items":[{"question":"What are Autoencoders?","answer":"<p>Autoencoders are a class of artificial neural networks used primarily for unsupervised learning tasks. They function by encoding input data into a compressed representation and then reconstructing the original input as accurately as possible from this representation. This process involves two primary components: an encoder and a decoder. Autoencoders are particularly useful for tasks such as dimensionality reduction, feature learning, and generative modeling.<\/p>"},{"question":"What is the history of Autoencoders?","answer":"<p>The concept of autoencoders originated in the 1980s with the development of the Hopfield Network. The term 'autoencoder' came into use as scientists started recognizing the unique self-encoding capabilities of these networks. Over the years, particularly with the advent of deep learning, autoencoders have found extensive use in areas like anomaly detection, noise reduction, and generative models.<\/p>"},{"question":"How does an Autoencoder work?","answer":"<p>An autoencoder works by encoding the input data into a compressed representation and then reconstructing the original input from this representation. This process involves two main components: an encoder, which transforms the input data into a compact code, and a decoder, which reconstructs the original input from the code. The objective of an autoencoder is to minimize the difference (or error) between the original input and the reconstructed output.<\/p>"},{"question":"What are the key features of Autoencoders?","answer":"<p>Autoencoders are data-specific, implying that they won't encode data for which they were not trained. They are also lossy, meaning that some information is always lost in the encoding process. Autoencoders are an unsupervised learning technique as they do not require explicit labels to learn the representation. Finally, they are often used for dimensionality reduction, where they can learn non-linear transformations of the data.<\/p>"},{"question":"What are the different types of Autoencoders?","answer":"<p>Several types of autoencoders exist, including Vanilla Autoencoder, Multilayer Autoencoder, Convolutional Autoencoder, Sparse Autoencoder, Denoising Autoencoder, and Variational Autoencoder (VAE). Each type of autoencoder has its unique characteristics and applications, ranging from basic dimensionality reduction to complex tasks like image recognition, feature selection, noise reduction, and generative modeling.<\/p>"},{"question":"How are Autoencoders used?","answer":"<p>Autoencoders have several applications, including data compression, image colorization, anomaly detection, denoising images, and generating new data. However, they can also pose challenges such as sensitivity to input data scale, difficulty determining the ideal architecture, the lack of interpretability of the compressed representation, and susceptibility to overfitting.<\/p>"},{"question":"How do Autoencoders compare with other techniques?","answer":"<p>Autoencoders are compared with other dimensionality reduction and unsupervised learning techniques based on several factors, including whether the technique is unsupervised, its ability to learn non-linear transformations, in-built feature selection capabilities, and whether it has generative capabilities. Compared to techniques like PCA, t-SNE, and K-means clustering, autoencoders often offer superior flexibility and performance, particularly in tasks involving non-linear transformations and generative modeling.<\/p>"},{"question":"What are the future perspectives on Autoencoders?","answer":"<p>Autoencoders are expected to play a significant role in future unsupervised and semi-supervised learning, anomaly detection, and generative modeling. Combining autoencoders with reinforcement learning or other generative models like Generative Adversarial Networks (GANs) is a promising avenue for creating more powerful generative models.<\/p>"},{"question":"How can Autoencoders be used with Proxy Servers?","answer":"<p>While autoencoders do not directly enhance the capabilities of a proxy server, they can be useful in systems where a proxy server is part of the network. Autoencoders can be used for data compression or for detecting anomalies in network traffic in such systems. Additionally, in the context of VPNs or other secure proxy servers, autoencoders could potentially be used to detect unusual or anomalous patterns in network traffic.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/475945","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\/475945\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/467668"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=475945"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}