Generalizing from a Few Examples: A Survey on Few-Shot Learning 小樣本學習最新綜述 | 三大數據增強方法

原文鏈接：小樣本學習與智能前沿

上一篇：A Survey on Few-Shot Learning | Introduction and Overview

本節中的FSL方法使用先驗知識來增強數據$D_{train}$，從而豐富了E中的監督信息。（圖4）。

Data augmentation via hand-crafted rules is usually used as pre-processing in FSL methods.
They can introduce different kinds of invariance for the model to capture. For example, on images, one can use translation [12, 76, 114, 119], flipping [103, 119], shearing [119], scaling [76, 160], reflection [34, 72], cropping [103, 160] and rotation [114, 138].

許多增強規則根據數據集制定，使得他們很難應用到其他數據集中。

因此manual data augmentation 不能完全解決FSL問題。

還有一些數據增強方式依賴於樣本是如何轉化和添加到訓練集的。我們把他們分類在Table 3當中。

下面，我們將分別介紹這三種方法。

01 Transforming Samples from Dtrain

這個策略通過轉換訓練集中原有的$(x_i,y_i)$爲多個樣本來增加訓練集$D_{train}$. 轉換過程作爲先驗知識包含在經驗E中，以便生成其他樣本。
早期的FSL論文[90]通過將每個樣本與其他樣本反覆對齊，從相似的類中學習了一組幾何變換。將學習到的變換應用於每個（xi，yi）以形成大數據集，然後可以通過標準機器學習方法學習大數據集。類似地，[116]從相似類中學習了一組自動編碼器，每個自動編碼器代表一個類內可變性。通過將習得的變化量添加到$x_i$來生成新樣本。在[53]中，通過假設所有類別在樣本之間共享一些可變換的可變性，可以學習單個變換函數，將從其他類別學習到的樣本對之間的差異轉移到（xi，yi）。在[74]中，不是枚舉成對的變量，而是使用從大量場景圖像中獲悉的一組獨立的屬性強度迴歸將每個$x_i$轉換爲幾個樣本，並將原始$x_i$的標籤分配給這些新樣本。[82]在[74]的基礎上進行了改進，將連續屬性子空間用於向x添加屬性變化。

02 Transforming Samples from a Weakly Labeled or Unlabeled Data Set

此策略通過從標記弱監督或未標記的大數據集中選擇帶有目標標記的樣本來增強Dtrain。例如，在用監控攝像頭拍攝的照片中，有人，汽車和道路，但沒有一個被標記。另一個示例是一段較長的演示視頻。它包含說話者的一系列手勢，但是沒有一個被明確註釋。由於此類數據集包含樣本的較大變化，因此將其增加到Dtrain有助於描繪更清晰的$p（x，y)$。而且，由於不需要人工來標記，因此收集這樣的數據集更加容易。但是，儘管收集成本很低，但主要問題是如何選擇帶有目標標籤的樣本以增加到Dtrain。在[102]中，爲Dtrain中的每個目標標籤學習了一個示例SVM，然後將其用於從弱標籤數據集中預測樣本的標籤。然後將具有目標標籤的樣品添加到Dtrain中。在[32]中，標籤傳播直接用於標記未標記的數據集，而不是學習分類器。在[148]中，採用漸進策略選擇信息豐富的未標記樣品。然後爲選定的樣本分配僞標籤，並用於更新CNN。

03 Transforming Samples from Similar Data Sets

該策略通過彙總和改編來自相似但較大數據集的輸入輸出對來增強$D_{train}$。 聚集權重通常基於樣本之間的某種相似性度量。 在[133]中，它從輔助文本語料庫中提取聚合權重[133]。 由於這些樣本可能不是來自目標FSL類，因此直接將彙總樣本增加到$D_{train}$可能會產生誤導。 因此，生成對抗網絡（GAN）[46]被設計爲從許多樣本的數據集中生成不可區分的合成x [42]。 它有兩個生成器，一個生成器將少拍類的樣本映射到大規模類，另一個生成器將大規模類的樣本映射到少數類（以彌補GAN訓練中樣本的不足） 。

Discussion and Summary

使用哪種增強策略的選擇取決於具體的應用。
有時，針對目標任務（或類）存在大量弱監督或未標記的樣本，但是由於收集註釋數據和/或計算成本高昂（這對應於引入的第三種情況）。 在這種情況下，可以通過轉換標記較弱或未標記的數據集中的樣本來執行增強。 當難以收集大規模的未標記數據集，但few-shot類具有某些相似類時，可以從這些相似類中轉換樣本。 如果只有一些學習的轉換器而不是原始樣本可用，則可以通過轉換訓練集中的原始樣本來進行擴充。

總的來說，通過增強$D_{train}$解決FSL問題非常簡單明瞭, 即通過利用目標任務的先驗信息來擴充數據。
另一方面，通過數據擴充來解決FSL問題的弱點在於，擴充策略通常是針對每個數據集量身定製的，並且不能輕易地用於其他數據集（尤其是來自其他數據集或域的數據。

最近，AutoAugment [27]提出了自動學習用於深度網絡訓練的增強策略的來解決這個問題。 除此之外，因爲生成的圖像可以很容易地被人在視覺上評估，現有的方法主要是針對圖像設計的。而文本和音頻涉及語法和結構較難生成。 [144]報告了最近對文本使用數據增強的嘗試。

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