1.2 Data Acquisition

1.2 Data Acquisition

Li Mu
Station B: https://space.bilibili.com/1567748478/channel/collectiondetail?sid=28144
Course homepage: https://c.d2l.ai/stanford-cs329p/

1. How to obtain the data

• Is there enough data?
  • If applicable: data processing
  • If not: Can you find the data, the source of the data?
    • Yes: discover data, merge data
    • No: Can the data be generated or the method of generating the data?
      • Yes: generate data

When there is not enough data, we can try to find additional other data and integrate them together as a data set.

If there is no way to obtain other additional data, we can also use data generation methods to increase data, such as: data enhancement (rotation, stretching, etc.) and use GAN to generate similar data (this will consider the issue of cost).

• How to find available datasets
  1. Datasets do exist, academic papers, conferences, competitions
  2. Find a benchmark dataset to evaluate performance: tuning: dataset diversification; deep network structure: large-scale dataset

2. Common datasets