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bluecdm / Long-tailed-recognition

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Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels

Python code for ICML 2023 paper entitled "Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels"

Test environment:

Requirements:

This code is based on:

Experiment procedure:

  1. (Set dataset directory) Change --data argument of sh/ImageNetLT_train_teacher.sh and sh/ImageNetLT_train_student.sh
  2. (Train teacher) Run bash ./sh/ImageNetLT_train_teacher.sh
  3. (Train student) Run bash ./sh/ImageNetLT_train_student.sh

Long-tailed recognition accuracy:

Dataset Backbone Epochs Top-1 Acc(%)
ImageNet-LT ResNeXt-50 90 58.3
ImageNet-LT ResNeXt-50 400 58.8
iNaturalist 2018 ResNet-50 100 73.1
iNaturalist 2018 ResNet-50 400 74.5
CIFAR-100-LT (imb. 100) ResNet-32 200 53.0
CIFAR-100-LT (imb. 50) ResNet-32 200 57.6
CIFAR-100-LT (imb. 10) ResNet-32 200 65.7
CIFAR-100-LT (imb. 100) ResNet-32 400 54.0
CIFAR-100-LT (imb. 50) ResNet-32 400 58.1
CIFAR-100-LT (imb. 10) ResNet-32 400 67.0