Code for "Long-tailed Distribution Adaptation"

Related tags

Deep Learning LDA
Overview

Long-tailed Distribution Adaptation (Accepted in ACM MM2021)

This project is built upon BBN.

Installation

pip install -r requirements.txt

Usage

  1. Training
python main/train.py --cfg configs/ImageNet.yaml DATASET.ROOT /path/ImageNet_ILSVRC2012
  1. Inference
python main/valid.py --cfg configs/ImageNet.yaml \
  TEST.MODEL_FILE ./output/LDA/ImageNet/LDA.ImageNet.resnext50.90epoch/models/best_model.pth \
  DATASET.VALID_JSON './datasets/ImageNet_LT/ImageNet_LT_test.txt'\
  DATASET.ROOT /path/ImageNet_ILSVRC2012
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Comments
  • Question about two classifiers.

    Question about two classifiers.

    Interesting work.

    In your paper: "With a single classifier ℎ (termed balanced classifier), it is difficult to minimize two homogeneous risk functions defined in Equation 4."

    This seems intuitive since it requires two fully connected layers for the classification of the two domains (Balanced and Unbalanced domains)

    However, I'm very curious about the results of using just one classifier for both Balanced and Unbalanced domains.

    Are there some experiments on this? Or can u pls give some insights of the reason behind this? (I suspect the effect may not be good using one classifier).

    Thanks in advance. Hope to ger your early replay.

    opened by qsunyuan 0
Owner
Zhiliang Peng
Zhiliang Peng
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