State of Computer vision

Data vs. Hand-Engineering

Two sources of knowledge

• Labeled data (x,y)
• Hand engineered features/network architecture/other components

For computer visions, because there not enough image data, many complex algorithms have been developed.

When you don’t have enough data hand-engineering is a very difficult, very skillful task that requires a lot of insight. And someone that is insightful with hand-engineering will get better performance.

Tips for doing well on benchmarks/winning competitions.

These may not be used for production as they have high computational cost.

• ensembling

  • Train several networks independently (3-15 networks) and average their outputs

• multi-crop at test time

  • Run classifier on multiple versions of test images and average results

  10 crops

One of the big problem with ensembling is that it requires a lots of memory to keep the indendent network, and computationally costly.