CNN bottleneck layer


## inception module
핵심은 1x1 Convolution
연산량을 최소화
연산량과 정보손실은 서로 tradeoff 관계라 연산량과 정보 손실의 관계를 잘 보면서 Error가 최소가 되는 지점
![](https://www.notion.so/image/https%3A%2F%2Fs3-us-west-2.amazonaws.com%2Fsecure.notion-static.com%2F59552677-4c80-46c9-b3fc-9678673bf88f%2FUntitled.png?table=block&id=4344af8f-0f0c-4470-bc0e-482e0f837efe&cache=v2)
[ResNet](https://texonom.com/resnet-d8c7707445684e848ebef939500672d4) 
![](https://www.notion.so/image/https%3A%2F%2Fs3-us-west-2.amazonaws.com%2Fsecure.notion-static.com%2Facc5b086-6258-4b9c-9aba-d9e0bcd5a220%2FUntitled.png?table=block&id=559e2275-d78b-4266-91ce-47404eab13c1&cache=v2)
### [GoogLeNet](https://texonom.com/googlenet-64b5ad51aafc48d492ba489f47ffa58a) 
![](https://www.notion.so/image/https%3A%2F%2Fs3-us-west-2.amazonaws.com%2Fsecure.notion-static.com%2F75fa731a-374c-4ea7-b572-2086555c79ca%2FUntitled.png?table=block&id=8c337e84-43f0-47cf-acf7-9b683cebfc88&cache=v2)




> [CNN의 Bottleneck에 대한 이해](https://velog.io/@lighthouse97/CNN의-Bottleneck에-대한-이해)






