小子样学习
Basic
Paper
Machine Learning： Learning Algorithm $\mathcal{A}$ helps Learners $\mathcal{F}$ improve certain performance measure $\mathcal{P}$ when executing some tasks, through precollected experiential Data $\mathcal{D}$. 通常需要大量的labeled data
SSL: 各种方法的分类
- Experience learning：
- augmented data：compensate the input data with other sources of data
- knowledge system:
- Representations from other domains (Transfer Learning)
- Trained models (Fine-tuing)
- Cognition knowledge on concepts: Such knowledge include common sense knowledge, domain knowledge, and other prior knowledge on the learned concept with small training samples. 例如想要识别眼睛的位置，可以告诉网络其在鼻子/嘴巴上方
- Meta knowledge：Some high-level knowledge beyond data. 例如1+1=2的meta knowledge就是告诉网络：1、2的意义，+、=的计算模式
- Concept learning： aims to perform recognition or form new concepts (samples) from few observations (samples) through fast processing (employs matching rule $\mathcal{R}$ to associate concepts in concept system $\mathcal{C}$ with input small samples $\mathcal{S}$)
- Concept system
- intensional representations of concept: precise definitions in proposition or semantic form on the learned concept, like its attribute characteristics 就是物体的属性，比如斑马的颜色是黑色+白色，而不是棕色or others
- extension representations of concept: prototypes and instances related to the learned concept. 是物体的原型/实例，如斑马的照片
- Matching rule: a procedure to associate concepts in concept system C with small samples S to implement a cognition or recognition task. The result tries to keep optimal in terms of performance measure P.
- Concept system
k-shot learning: （just describes a setting manner of the SSL problem）详细介绍