第零阶段：看分割看不懂
这个阶段典型表现是，对于梯度下降，交叉熵损失，激活函数等概念模棱两可，这可能是博主说的零基础入门。你可能需要对深度学习和计算机视觉的基本任务有一个较为系统的学习，这块应该转看深度学习入门等相关话题，斯坦福的李飞飞cs231n被推荐的挺多的。这块我就不多赘述了，我更多分享后面三个阶段关于分割的学习。
第一阶段：看分割是分割
这个阶段的典型表现是，有了对深度学习和计算机视觉的基础，开始痴迷于各种分割网络和loss改进，对benchmark的每一个性能的改进都有很好的follow。这个阶段应该梳理以下问题。
-
语义/实例/全景分割的问题难点在哪里 -
benchmark数据集有哪些 -
评价指标是什么 -
现有的方法有哪些，关系是什么 -
还有哪些可以改进的地方
理论搞的差不多，我们开始上一些code部分。这里推荐几个优秀的分割的repo，供入门的同学学习。
第二阶段:看分割是CV
第三阶段:看分割是learning
比如在Indirect Local Attacks for Context-aware Semantic Segmentation Networks中，PSPNet和PSANet等SOTA分割模型被局部攻击后，效果还不如FCN baseline，是非常有意思的发现。Google的 Self-training with Noisy Student improves ImageNet classification，已经不再是从(人工)网络设计上去提升性能，而是通过一种半监督的方式，利用更多的无监督的数据进行迭代学习去提升分类的性能。何凯明的Momentum Contrast for Unsupervised Visual Representation Learning提出了一种新的无监督范式，在7个分割/检测的benchmark上得到了提升。
结语
原文链接：
https://www.zhihu.com/question/322077321/answer/925208015
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