PyTorch implementation of the paper: Long-tail Learning via Logit Adjustment

Overview

logit-adj-pytorch

PyTorch implementation of the paper: Long-tail Learning via Logit Adjustment

This code implements the paper: Long-tail Learning via Logit Adjustment : Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar. ICLR 2021.

Running the code

# To produce baseline (ERM) results:
python main.py --dataset cifar10-lt

# To produce posthoc logit-adjustment results:
python main.py --dataset cifar10-lt  --logit_adj_post 1

# To produce logit-adjustment loss results:
python main.py --dataset cifar10-lt  --logit_adj_train 0

# To monitor the training progress using Tensorboard:
tensorboard --logdir logs

Replace cifar10-lt above with cifar100-lt to obtain results for the CIFAR-100 long-tail dataset.

Results

Baseline Post-hoc logit adjustment Logit-adjusted loss
CIFAR10LT 0.7127 0.7816 0.7857
CIFAR100LT 0.3985 0.4404 0.4402
Owner
Chamuditha Jayanga
Electronics and Telecommunication Engineering Undergraduate at University of Moratuwa, Sri Lanka
Chamuditha Jayanga
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