Official PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)

Overview

MeTAL - Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning (ICCV2021 Oral)

Sungyong Baik, Janghoon Choi, Heewon Kim, Dohee Cho, Jaesik Min, Kyoung Mu Lee

Official PyTorch implementation of Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning (ICCV2021 Oral)

The code is based off the public code of MAML++, where their reimplementation of MAML is used as the baseline. The code also includes the implementation of ALFA.

[Paper-arXiv] [Video]

Requirements

Ubuntu 18.04

  • Anaconda3
  • Python==3.7.10
  • PyTorch==1.4
  • numpy==1.19.2

To install requirements, first download Anaconda3 and then run the following:

conda create -n metal python=3.7.10
conda activate metal
bash install.sh

Datasets

For miniIamgenet, the dataset can be downloaded from the link provided from MAML++ public code. make a directory named 'datasets' and place the downloaded miniImagnet under the 'datasets' directory.

Training

To train a model, run the following command in experiments_scripts directory

bash MeTAL.sh $GPU_ID

Evaluation

After training is finished, evaluation is performed automatically. To run an evaluation manually, run the same command

bash MeTAL.sh $GPU_ID

Results

Model Backbone 1-shot Accuracy 5-shot Accuracy
MAML 4-CONV 49.64 ± 0.31% 64.99 ± 0.27%
MeTAL 4-CONV 52.63 ± 0.37% 70.52 ± 0.29%
ALFA+MAML 4-CONV 50.58 ± 0.51% 69.12 ± 0.47%
ALFA+MeTAL 4-CONV 57.75 ± 0.38% 74.10 ± 0.43%
MAML ResNet12 58.60 ± 0.42% 69.54 ± 0.38%
MeTAL ResNet12 59.64 ± 0.38% 76.20 ± 0.19%
ALFA+MAML ResNet12 59.74 ± 0.49% 77.96 ± 0.41%
ALFA+MeTAL ResNet12 66.61 ± 0.28% 81.43 ± 0.29%

Reference

@InProceedings{baik2021meta,
 title={Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning},
 author={Sungyong Baik, Janghoon Choi, Heewon Kim, Dohee Cho, Jaesik Min, Kyoung Mu Lee}
 booktitle = {International Conference on Computer Vision (ICCV)}, 
 year={2021}
}
Owner
Sungyong Baik
Ph.D. Student in CVLab, SNU
Sungyong Baik
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