ElementAI / Embedding Propagation
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Embedding Propagation
Smoother Manifold for Few-Shot Classification [Paper] (ECCV2020)
Embedding propagation can be used to regularize the intermediate features so that generalization performance is improved.
Usage
Add an embedding propagation layer to your network.
pip install git+https://github.com/ElementAI/embedding-propagation
import torch
from embedding_propagation import EmbeddingPropagation
ep = EmbeddingPropagation()
features = torch.randn(32, 32)
embeddings = ep(features)
Experiments
Generate the results from the [Paper].
Install requirements
pip install -r requirements.txt
This command installs the Haven library which helps in managing the experiments.
Download the Datasets
If you have the pkl version of miniimagenet, you can still use it by setting the dataset name to "episodic_miniimagenet_pkl", in each of the files in exp_configs.
Reproduce the results in the paper
1. Pre-training
python3 trainval.py -e pretrain -sb ./logs/pretraining -d <datadir>
where <datadir> is the directory where the data is saved.
2. Fine-tuning
In exp_configs/finetune_exps.py, set "pretrained_weights_root": ./logs/pretraining/
python3 trainval.py -e finetune -sb ./logs/finetuning -d <datadir>
3. SSL experirments with 100 unlabeled
In exp_configs/ssl_exps.py, set "pretrained_weights_root": ./logs/finetuning/
python3 trainval.py -e ssl_large -sb ./logs/ssl/ -d <datadir>
4. SSL experirments with 20-100% unlabeled
In exp_configs/ssl_exps.py, set "pretrained_weights_root": ./logs/finetuning/
python3 trainval.py -e ssl_small -sb ./logs/ssl/ -d <datadir>
Results
| dataset | model | 1-shot | 5-shot |
|---|---|---|---|
| episodic_cub | conv4 | 65.94 ± 0.93 | 78.80 ± 0.64 |
| episodic_cub | resnet12 | 81.32 ± 0.84 | 91.02 ± 0.44 |
| episodic_cub | wrn | 87.48 ± 0.68 | 93.74 ± 0.35 |
| episodic_miniimagenet | conv4 | 57.41 ± 0.85 | 72.35 ± 0.62 |
| episodic_miniimagenet | resnet12 | 64.82 ± 0.89 | 80.59 ± 0.64 |
| episodic_miniimagenet | wrn | 69.92 ± 0.81 | 83.64 ± 0.54 |
| episodic_tiered-imagenet | conv4 | 58.63 ± 0.92 | 72.80 ± 0.78 |
| episodic_tiered-imagenet | resnet12 | 75.90 ± 0.90 | 86.83 ± 0.58 |
| episodic_tiered-imagenet | wrn | 78.46 ± 0.90 | 87.46 ± 0.62 |
Different from the paper, these results were obtained on a run with fixed hyperparameters during fine-tuning: lr=0.001, alpha=0.2 (now default), train_iters=600, classification_weight=0.1
Citation
@article{rodriguez2020embedding,
title={Embedding Propagation: Smoother Manifold for Few-Shot Classification},
author={Pau Rodríguez and Issam Laradji and Alexandre Drouin and Alexandre Lacoste},
year={2020},
journal={arXiv preprint arXiv:2003.04151},
}