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MAE無監督學習方法在多個下游任務中優于監督方法-行業數據

MAE無監督學習方法在多個下游任務中優于監督方法
MAE無監督學習方法在多個下游任務中優于監督方法
想的 MoCo 問世，證明了無監督學習在計算機視覺領域能取得不亞于監督學習的效果。在此之后，基于對比學習的無監督學習方法不斷演進，朝著結構更簡單、對數據樣本要求更低、更容易應用的方向發展，準確度也不斷提升。
行業數據 下載Excel 下載圖片 原圖定位
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MAE無監督學習方法在多個下游任務中優于監督方法_第1頁
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MAE無監督學習方法在多個下游任務中優于監督方法_第3頁
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MAE無監督學習方法在多個下游任務中優于監督方法_第4頁
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MAE無監督學習方法在多個下游任務中優于監督方法_第8頁
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MAE無監督學習方法在多個下游任務中優于監督方法_第9頁
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所屬報告： 計算機行業：大模型時代AI技術向效率提升演進-230227（12頁）.pdf
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