https://github.com/baiksung/MeTAL
Official PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)
https://github.com/baiksung/MeTAL
computer-vision deep-learning few-shot-learning iccv machine-learning maml meta-learning pytorch
Last synced: about 1 year ago
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Official PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)
- Host: GitHub
- URL: https://github.com/baiksung/MeTAL
- Owner: baiksung
- Created: 2021-08-14T19:15:41.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2021-12-18T01:41:34.000Z (over 4 years ago)
- Last Synced: 2024-11-15T06:32:06.672Z (over 1 year ago)
- Topics: computer-vision, deep-learning, few-shot-learning, iccv, machine-learning, maml, meta-learning, pytorch
- Language: Python
- Homepage:
- Size: 146 KB
- Stars: 62
- Watchers: 2
- Forks: 13
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
# 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)](https://openaccess.thecvf.com/content/ICCV2021/papers/Baik_Meta-Learning_With_Task-Adaptive_Loss_Function_for_Few-Shot_Learning_ICCV_2021_paper.pdf)
The code is based off the public code of [MAML++](https://github.com/AntreasAntoniou/HowToTrainYourMAMLPytorch), where their reimplementation of MAML is used as the baseline.
The code also includes the implementation of [ALFA](https://github.com/baiksung/ALFA).
\[[Paper-arXiv](http://arxiv.org/abs/2110.03909)\] \[[Video](https://youtu.be/LHpNSSPsq_Y)\]
## 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:
```setup
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](https://drive.google.com/file/d/1qQCoGoEJKUCQkk8roncWH7rhPN7aMfBr/view) provided from [MAML++ public code](https://github.com/AntreasAntoniou/HowToTrainYourMAMLPytorch).
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
bash MeTAL.sh $GPU_ID
```
## Evaluation
After training is finished, evaluation is performed automatically.
To run an evaluation manually, run the same command
```bash
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}
}
```