SWA (Stochastic Weight Averaging)

  • 신경망 모델 다수를 앙상블링 하는 방법은 대회등에서 꾸준히 정확도를 높이기 위해 사용되어왔던 방법
  • 다수의 여러개의 모델을 만들기 위해서는 하나를 만들때보다 N배의 컴퓨팅 자원 필요. 이를 효율적으로 학습하고자 한번의 학습으로만 다양한 모델을 반영하도록 유도
    • Snapshot Ensembling – SGD가 local optima (국소최적화) 지점을 찾기위해 계속 공간을 돌아다니도록 하고, 이를 반복하여 다양한 가중치들을 찾고, 이를 평균냄
    • Fast Geometric Ensembling (FGE) – 훈련율 (learning rate)를 consine대신 linear하게 설정하여 조금더 빠르게 학습이 될수 있도록 함. 여러 골짜기(local optima)들이 중간중간 박혀있는 것이 아니라, 보통은 계곡이 계속되기 때문
  • SWA에서는 굳이 여러 모델들의 파라미터를 가지고 있지않고, 프로그래밍에서 값을 업데이트 하는 것처럼 weight를 업데이트 한 다음, 최종적으로 돌때는 이미 업데이트가 되어있는 방식
여러개의 국소값들을 찾아 이를 합치는 방식 (Snapshot Ensembling)
실질적인 국소 최적화 지점 (Fast Geometric Ensembling)
Stochastic Weight Averagin 에서 한번씩 값을 업데이트 하는 방법

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