Lecture 2: Image Classification

Introduction

Image classification is the task of assigning a label…

can be a building-block for many applications

More robust, data-driven approaches

Understanding the dataset

• 简单介绍一下类似于MNIST, CIFAR-100等数据集的基本结构
• 提出Omniglot数据集的概念 few-shot learning

Choosing a model

Nearest Neighbor

• find the distance metric between the test image and all the training images
• memorize the training images and their corresponding labels
• predict the label of the test image based on the nearest training image

With N examples…

• training time: O(1) or O(N), depending on the copying strategy
• testing time: O(N)

there are more knn… see here

决策边界平滑化

• more neighboring examples， k 🆙
• change the metric

Evaluating the model

详见DATA-100课程 train / validation / test set的划分 & k-fold cross-validation的介绍

通用近似定理

knn可以拟合任意的连续函数

维度灾难

总结