# CrossDomainFewShot

**Repository Path**: zc0617/CrossDomainFewShot

## Basic Information

- **Project Name**: CrossDomainFewShot
- **Description**: Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (ICLR 2020 spotlight)
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-11-29
- **Last Updated**: 2021-11-29

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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 (htseng6@ucmerced.edu)

## 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<br>
[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/)<br>
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.