April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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第十七章：使用自動編碼網路與 GAN 來進行表徵學習與生成學習
那麼
，GAN
如何利用這個理論
？
論文的作者說
，GAN
只能取得一種納許平衡
：
也就是當
生成網路產生完全擬真的圖像
，
迫使鑑別網路必須用猜的時
（50%
是真的
，50%
是假的
）。
這個事實非常鼓舞人心
：
似乎你只要訓練
GAN
夠久
，
它最終就會到達這種平衡狀態
，
給你一個完美的生成網路
。
遺憾的是
，
事情沒這麼簡單
：
沒有人可以保證一定可以取得
平衡
。
最大的挑戰稱為
模式崩潰
（mode collapse），
也就是生成網路的輸出逐漸變得單調
。
為何如
此
？
假設生成網路可以產生很有說服力的鞋子
，
但沒辦法產生有說服力的其他種類
，
因為
鞋子比較有機會騙過鑑別網路
，
所以生成網路產生更多鞋子圖片
，
漸漸地
，
它忘了如何產
生任何其他物品
。
同時
，
因為鑑別網路只能看到鞋子的假圖
，
所以它也會忘了如何鑑別其
他種類的圖片
。
最後
，
當鑑別網路可以成功地鑑別鞋子的真假時
，
生成網路會被迫處理其
他的種類
，
它可能變得比較擅長處理襯衫
，
忘記鞋子
，
鑑別網路也會跟隨這個傾向
。GAN
會在幾個種類之間漸漸地循環
，
永遠無法非常擅長任何一種
。
此外
，
因為生成網路與鑑別網路不斷互相對抗
，
它們的參數可能是振盪且不穩定的
。
訓練
在一開始可能是正常的
，
接著因為這些不穩定性
，
而毫無理由地發散
。
而且因為有很多因
素會影響這些複雜的動態
，GAN
對超參數非常敏感
：
你可能要花很多精力微調它們
。
這些問題讓研究人員從
2014
年就陷入忙碌的狀態
，
有許多論文探討這個主題
，
有些提出
新的代價函數
11
（
儘管
Google
研究員
2018 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.