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https://github.com/nupurkmr9/S2M2_fewshot
https://github.com/nupurkmr9/S2M2_fewshot
Last synced: about 2 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/nupurkmr9/S2M2_fewshot
- Owner: nupurkmr9
- License: other
- Created: 2019-10-18T18:36:25.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-01-19T11:30:34.000Z (almost 4 years ago)
- Last Synced: 2024-07-31T23:45:03.513Z (4 months ago)
- Language: Python
- Size: 106 KB
- Stars: 111
- Watchers: 5
- Forks: 20
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
- awesome-few-shot-meta-learning - code - official (PyTorch) from CloserLookFewShot
README
S2M2
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
=======
A few-shot classification algorithm: [Charting the Right Manifold: Manifold Mixup for Few-shot Learning](https://arxiv.org/pdf/1907.12087.pdf)
Our code is built upon the code base of [A Closer Look at Few-shot Classification](https://openreview.net/pdf?id=HkxLXnAcFQ) and [Manifold Mixup: Better Representations by Interpolating Hidden States](http://proceedings.mlr.press/v97/verma19a.html)
Running the code
------------
***Donwloading the dataset and create base/val/novel splits***:
miniImageNet
* Change directory to filelists/miniImagenet/
* Run 'source ./download_miniImagenet.sh'
CUB
* Change directory to filelists/CUB/
* Run 'source ./download_CUB.sh'
CIFAR-FS
* Change directory to filelists/cifar/
* Run 'source ./download_cifar.sh'
**Training**
DATASETNAME: miniImagenet/cifar/CUB/tieredImagenet
METHODNAME: S2M2_R/rotation/manifold_mixup
For CIFAR-10
python train_cifar.py --method [METHODNAME] --model WideResNet28_10 --batch_size --stop_epoch
For miniImagenet/CUB/tieredImagenet
python train.py --dataset [DATASETNAME] --method [METHODNAME] --model WideResNet28_10 --batch_size --stop_epoch
* For tieredImagenet Novel/Base split taken from https://github.com/renmengye/few-shot-ssl-public
------------
**Example Training script to replicate our result on CUB Dataset**:
* python train.py --dataset CUB --method rotation --model WideResNet28_10 --stop_epoch 200 --batch_size 64 --test_batch_size 16
* python train.py --dataset CUB --method S2M2_R --model WideResNet28_10 --stop_epoch 100 --batch_size 64 --test_batch_size 16
**Fetching pretrained WideResNet_28_10 model checkpoints for evaluation**
Directory path to save models should be: checkpoints/[DATASETNAME]/WideResNet28_10_[METHODNAME]/
Pre-trained mdoels can be downloadeded from [https://drive.google.com/open?id=1S-t56H8YWzMn3sjemBcwMtGuuUxZnvb_](https://drive.google.com/open?id=1S-t56H8YWzMn3sjemBcwMtGuuUxZnvb_). Move the tar files for each dataset into 'checkpoints' folder and untar it if required.
**Few-shot evaluation**
Create an empty 'features' directory inside 'S2M2'
python save_features.py --dataset [DATASETNAME] --method [METHODNAME] --model WideResNet28_10
python test.py --dataset [DATASETNAME] --method [METHODNAME] --model WideResNet28_10 --n_shot [1/5]
Features of pre-trained network can also be be directly downloaded at this link 'https://drive.google.com/open?id=1JtA7p3sDPksvBmOsJuR4EHw9zRHnKurj' for easy evaluation without the need to download datasets and models. Move the tar files for each dataset into 'features' folder and untar it.
**Comparison with prior/current state-of-the-art methods on mini-ImageNet, CUB and CIFAR-FS dataset.**
Note: We implemented LEO on CUB dataset. Other numbers are reported directly from the paper.
| Method | mini-ImageNet | | CUB | | CIFAR-FS | |
|:--------------:|:-------------:|:-------------:|:-------------:|:-------------:|:--------------:|:-------------:|
| | 1-shot | 5-shot | 1-shot | 5-shot | 1-shot | 5-shot |
| Baseline++ | 57.33 +- 0.10 | 72.99 +- 0.43 | 70.4 +- 0.81 | 82.92 +-0.78 | 67.5 +- 0.64 | 80.08 +- 0.32 |
| LEO | 61.76 +- 0.08 | 77.59 +- 0.12 | 68.22+- 0.22 | 78.27 +- 0.16 | - | - |
| DCO | 62.64 +- 0.61 | 78.63 +- 0.46 | - | - | 72.0 +- 0.7 | 84.2 +- 0.5 |
| Manifold Mixup | 57.6 +- 0.17 | 75.89 +- 0.13 | 73.47 +- 0.89 | 85.42 +- 0.53 | 69.20 +- 0.2 | 83.42 +- 0.15 |
| Rotation | 63.9 +- 0.18 | 81.03 +- 0.11 | 77.61 +- 0.86 | 89.32 +- 0.46 | 70.66 +- 0.2 | 84.15 +- 0.14 |
| S2M2_R | 64.93 +- 0.18 | 83.18 +- 0.11 | 80.68 +- 0.81 | 90.85 +- 0.44 | 74.81 +- 0.19 | 87.47 +- 0.13 |
If you use this code for your research, Please cite using
```
@inproceedings{mangla2020charting,
title={Charting the right manifold: Manifold mixup for few-shot learning},
author={Mangla, Puneet and Kumari, Nupur and Sinha, Abhishek and Singh, Mayank and Krishnamurthy, Balaji and Balasubramanian, Vineeth N},
booktitle={The IEEE Winter Conference on Applications of Computer Vision},
pages={2218--2227},
year={2020}
}
```
References
------------
[A Closer Look at Few-shot Classification](https://openreview.net/pdf?id=HkxLXnAcFQ)
[Meta-Learning with Latent Embedding Optimization](https://arxiv.org/pdf/1807.05960.pdf)
[Meta Learning with Differentiable Convex Optimization](https://arxiv.org/pdf/1904.03758.pdf)
[Manifold Mixup: Better Representations by Interpolating Hidden States](http://proceedings.mlr.press/v97/verma19a.html)