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https://github.com/hytseng0509/CrossDomainFewShot
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (ICLR 2020 spotlight)
https://github.com/hytseng0509/CrossDomainFewShot
domain-generalization few-shot-learning iclr2020 meta-learning
Last synced: 3 months ago
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Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (ICLR 2020 spotlight)
- Host: GitHub
- URL: https://github.com/hytseng0509/CrossDomainFewShot
- Owner: hytseng0509
- Created: 2020-01-15T05:15:19.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-04-12T11:14:23.000Z (about 4 years ago)
- Last Synced: 2024-01-08T11:25:36.188Z (6 months ago)
- Topics: domain-generalization, few-shot-learning, iclr2020, meta-learning
- Language: Python
- Size: 71.3 KB
- Stars: 312
- Watchers: 8
- Forks: 61
- Open Issues: 30
-
Metadata Files:
- Readme: README.md
Lists
- awesome-stars - hytseng0509/CrossDomainFewShot - Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (ICLR 2020 spotlight) (Python)
- awesome-few-shot-meta-learning - code - official (PyTorch)
README
# Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
[[Project Page]](http://vllab.ucmerced.edu/ym41608/projects/CrossDomainFewShot)[[Paper]](https://arxiv.org/abs/2001.08735)
Pytorch implementation for our cross-domain few-shot classification method. With the proposed learned feature-wise transformation layers, we are able to:
1. improve the performance of exisiting few-shot classification methods under **cross-domain** setting
2. achieve stat-of-the-art performance under **single-domain** setting.
Contact: Hung-Yu Tseng ([email protected])
## Paper
Please cite our paper if you find the code or dataset useful for your research.
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
[Hung-Yu Tseng](https://sites.google.com/site/hytseng0509/), [Hsin-Ying Lee](http://vllab.ucmerced.edu/hylee/), [Jia-Bin Huang](https://filebox.ece.vt.edu/~jbhuang/), [Ming-Hsuan Yang](http://faculty.ucmerced.edu/mhyang/)
International Conference on Learning Representations (ICLR), 2020 (**spotlight**)
```
@inproceedings{crossdomainfewshot,
author = {Tseng, Hung-Yu and Lee, Hsin-Ying and Huang, Jia-Bin and Yang, Ming-Hsuan},
booktitle = {International Conference on Learning Representations},
title = {Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation},
year = {2020}
}
```
## Usage
### Prerequisites
- Python >= 3.5
- Pytorch >= 1.3 and torchvision (https://pytorch.org/)
- You can use the `requirements.txt` file we provide to setup the environment via Anaconda.
```
conda create --name py36 python=3.6
conda install pytorch torchvision -c pytorch
pip3 install -r requirements.txt
```
### Install
Clone this repository:
```
git clone https://github.com/hytseng0509/CrossDomainFewShot.git
cd CrossDomainFewShot
```
### Datasets
Download 5 datasets seperately with the following commands.
- Set `DATASET_NAME` to: `cars`, `cub`, `miniImagenet`, `places`, or `plantae`.
```
cd filelists
python3 process.py DATASET_NAME
cd ..
```
- Refer to the instruction [here](https://github.com/wyharveychen/CloserLookFewShot#self-defined-setting) for constructing your own dataset.
### Feature encoder pre-training
We adopt `baseline++` for MatchingNet, and `baseline` from [CloserLookFewShot](https://github.com/wyharveychen/CloserLookFewShot) for other metric-based frameworks.
- Download the pre-trained feature encoders.
```
cd output/checkpoints
python3 download_encoder.py
cd ../..
```
- Or train your own pre-trained feature encoder (specify `PRETRAIN` to `baseline++` or `baseline`).
```
python3 train_baseline.py --method PRETRAIN --dataset miniImagenet --name PRETRAIN --train_aug
```
### Training with multiple seen domains
Baseline training w/o feature-wise transformations.
- `METHOD` : metric-based framework `matchingnet`, `relationnet_softmax`, or `gnnnet`.
- `TESTSET`: unseen domain `cars`, `cub`, `places`, or `plantae`.
```
python3 train_baseline.py --method METHOD --dataset multi --testset TESTSET --name multi_TESTSET_ori_METHOD --warmup PRETRAIN --train_aug
```
Training w/ learning-to-learned feature-wise transformations.
```
python3 train.py --method METHOD --dataset multi --testset TESTSET --name multi_TESTSET_lft_METHOD --warmup PRETRAIN --train_aug
```
### Evaluation
Test the metric-based framework `METHOD` on the unseen domain `TESTSET`.
- Specify the saved model you want to evaluate with `--name` (e.g., `--name multi_TESTSET_lft_METHOD` from the above example).
```
python3 test.py --method METHOD --name NAME --dataset TESTSET
```
## Note
- This code is built upon the implementation from [CloserLookFewShot](https://github.com/wyharveychen/CloserLookFewShot).
- The dataset, model, and code are for non-commercial research purposes only.
- You can change the number of shot (i.e. 1/5 shots) using the argument `--n_shot`.
- You need a GPU with 16G memory for training the `gnnnet` approach w/ learning-to-learned feature-wise transformations.
- 04/2020: We've corrected the code for training with multiple domains. Please find the link [here](http://vllab.ucmerced.edu/ym41608/projects/CrossDomainFewShot/checkpoints/multi_cub_lft_relationnet_softmax.tar.gz) for the model trained with the current implementation on Pytorch 1.4.