[92] Long-Tail Learning via Logit Adjustment

TL;DR

I read this because.. : #57 에서 사용한 trick
task : long-tail image classification
problem : real-world에서는 class가 imbalance한 경우가 많다
idea : label frequency 기반으로 logit adjustment를 함
architecture : ResNet-32, ResNet-50
objective : softmax의 exponential에 들어가는 값에다가 class별 frequency를 $\tau$를 곱해서 더함.
baseline : ERM, weight normalisation, Adaptive, Equalized
data : CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT, iNaturalist2018
evaluation : balanced error(class별 평균)
result : outperform baselines
limitation / things I cannot understand : 수식 및 논리를 이해하지는 않고 읽음