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0_275_277.mp3 | 所 以 是 在 一 個 這 個 loss 比 較 低 的 位 置 | [
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|
0_346_347.mp3 | 好 那 怎 麼 做 呢 | [
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0_911_913.mp3 | 假 設 只 有 在 這 個 方 向 上 移 動 | [
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0_952_954.mp3 | 這 個 橫 軸 是 什 麼 | [
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0_968_971.mp3 | 這 三 個 任 務 依 序 訓 練 的 結 果 | [
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0_1022_1024.mp3 | 接 下 來 開 始 學 任 務 b 了 | [
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0_1046_1048.mp3 | catastrophic forgetting 的 效 果 | [
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0_1172_1174.mp3 | 結 果 還 要 更 好 的 | [
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0_1208_1210.mp3 | 就 列 了 一 大 堆 方 法 | [
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0_1610_1611.mp3 | 另 外 兩 個 做 法 | [
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0_1918_1920.mp3 | 給 第 二 個 任 務 的 classifier 做 訓 練 | [
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|
0_1999_2000.mp3 | 如 果 你 想 想 看 | [
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|
0_2044_2047.mp3 | incremental classifier and representation learning | [
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1_24_26.mp3 | 我 們 已 經 學 到 各 式 各 樣 的 | [
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1_26_28.mp3 | network 架 構 | [
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1_32_34.mp3 | 如 果 是 一 張 圖 片 的 時 候 怎 麼 辦 | [
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1_46_48.mp3 | 那 我 們 目 前 有 講 過 的 network 架 構 | [
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1_51_53.mp3 | 你 日 後 會 遇 到 的 問 題 了 | [
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|
1_60_62.mp3 | 當 做 一 個 generator 來 用 | [
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|
1_66_67.mp3 | 那 把 network 拿 來 | [
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|
1_67_69.mp3 | 當 作 generator 使 用 | [
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1_85_87.mp3 | 所 以 現 在 network 它 不 是 只 看 | [
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|
1_93_94.mp3 | 那 你 可 能 會 問 說 | [
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1_94_97.mp3 | network 怎 麼 同 時 看 x 跟 z 呢 | [
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|
1_97_98.mp3 | 那 這 個 有 很 多 不 同 的 做 法 啦 | [
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|
1_98_100.mp3 | 就 看 你 怎 樣 設 計 你 的 network 架 構 | [
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1_107_109.mp3 | 就 當 作 network 的 input 這 樣 可 以 嗎 | [
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1_110_114.mp3 | 或 者 是 你 的 x 跟 z 正 好 長 度 一 模 一 樣 | [
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1_114_115.mp3 | 把 它 們 相 加 以 後 | [
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|
1_115_116.mp3 | 當 做 network 的 input | [
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1_129_131.mp3 | sample 出 來 的 | [
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1_133_134.mp3 | 這 邊 有 一 個 限 制 是 | [
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|
1_147_149.mp3 | 舉 例 來 說 這 個 distribution | [
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|
1_149_151.mp3 | 可 以 是 一 個 function distribution | [
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1_153_154.mp3 | 你 知 道 怎 麼 從 | [
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1_165_166.mp3 | 所 以 這 一 個 distribution | [
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1_168_171.mp3 | 但 你 只 要 記 得 說 它 是 簡 單 的 | [
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1_179_180.mp3 | 裡 面 做 一 個 sample | [
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1_182_185.mp3 | 隨 著 你 sample 到 的 z 不 同 | [
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1_200_201.mp3 | 我 們 這 邊 每 次 sample 到 | [
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1_203_205.mp3 | 通 過 一 個 複 雜 的 network 轉 換 以 後 | [
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1_208_209.mp3 | 你 的 network 的 輸 出 | [
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|
1_212_213.mp3 | 那 這 種 可 以 輸 出 | [
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|
1_213_214.mp3 | 一 個 distribution 的 network | [
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