本篇論文提出深度學習的架構,透過半監督式的機制與生成對抗式網
路的學習,訓練語意分割模型,進而對影像分割做出更精確以期接近全監
督式訓練的預測。
半監督式生成對抗語意分割任務在主架構上皆起源於典型的對抗式網
路概念。基礎網路必須構建兩個主要區塊,一個是語意分割器亦或是分類
器來實現圖像分割的目的。第二,用來強化模型分割能力的判別器。
以生成對抗式網路發展出的架構概念主要分為兩類。第一類,利用現
有的分割網路架構作為一個可靠且穩定的生成模型,再以判別模型強化生
成網路的分割能力。第二類,以生成網路產生擬真的合成影像擴增訓練樣
本以及多樣性,再佐以分類器進而判斷類別,從而達到分割器的效果。前
者優勢在於網路分割架構的穩定性,縱使標記樣本大量縮減,模型仍存在
一定的再現性與語意分類表現。後者優勢在於訓練樣本的擴增,並施加分
類器促進圖像真實性和語意分割能力。
我提出的方法利用既有的語意分割模型與生成多樣分割圖樣擴增,將
兩種實作優勢結合並搭配半監督式策略的選擇性訓練,即選擇性採取有效
且可靠的分割區塊進行損失函數訓練穩定且有效的利用未標記樣本。除了
只採用未標記圖像所生成之可靠分割進行訓練,更進一步利用相較可靠樣
本而言更為大量的不可靠之分割結果,使得網路認識並降低不可靠的分類
之機率以進一步減少語意判斷錯誤的可能,提升整體平均語意分割之準確
率。

We proposed a deep learning architecture based on semi supervision and
generative adversarial learning to train the semantic segmentation network expecting
to segment more precisely and as precise as fully-supervised training.
Semi-supervised semantic segmentation network based on generative adversarial
learning is derived from typical GAN’s concept. The architecture has to have two main
blocks: one is a segmentation network or a classifier used to do the segment job.
Another block is a discriminator used to enhance the segmentation capability of the
model.
There are mainly two kinds of approach used to solve this task based on using
adversarial learning. The first one takes an existing segmentation network as a reliable
and stable generation model and fortifies the segmentation ability by a discriminator.
The second one trains a generator to augment training examples in the dataset and use
a classifier to serve as a discriminator. The advantage of the former comes from the
stability and reliability of the segmentation network. Even if the labeled data are limited,
the network still preserves a certain extent of model reproducibility. The advantage of
the later is the amount of augmented data generated and implement a classifier to boost
the reality of the image and ability of segmentation the objects.
We proposed a method using existed segmentation model and a generator for
synthesizing variations of segmentation maps. Also, we combine the architecture with
a selective strategy to train the segmentation network. So as to say, we selectively
choose the trusted regions to train the cross-entropy loss and filter out the untrusted
classes in untrusted regions to make the network lowers the possibilities of classifying
them as the ground-truth. Our approach leverage each and every pixel information in
an image with careful supervising helping the segmentation network avoid making
mistakes and improves the mean-IOU.

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