论文阅读笔记《Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification》
核心思想 本文提出一种基于最大熵图块采样算法的强化学习模型来解决小样本学习问题。作者首先提出常见的目标分类网络都是把一整张图片作为输入，进行特征提取，然后分类。而人类在观察事物的时候通常都会把注意力集中在部分感兴趣的区域，也就是所谓的注意力机制，而且当我们第二次看一个物体时，我们的注意力会沿着一定的轨迹移动，也就是说我们每次观察物体的一部分图块，经过多次观察后识别该物体。本文正是利用这一思想，
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相关文章
- 1. 【论文阅读笔记】Hyperspectral image classification via a random patches network
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