Open
Conversation
Collaborator
|
잘 적어주셨습니다! inductive bias로 데이터 효율이 높다는 장점과 연산 효율로 다양한 downstream task에서 backbone으로 활용되는 모델이죠. 수고하셨습니다 은빈님~~ |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
ResNet은 CNN기반 모델로 CNN 특유의 inductive bias 특성으로 인해 데이터가 제한적인 환경에서도 안정적인 성능을 보임. 더불어, convolution 연산을 수행해 상대적으로 계산 비용이 효율적이고 detection이나 segmentation에서 백본으로 활용됨
False로 설정하면 학습이 부안정, 정확도 낮고 수렴 속도가 느림
True로 설정하면 빠르게 수렴, 높은 정확도