ServiceNow completed its acquisition of Element AI on January 8, 2021. All references to Element AI in the materials that are part of this project should refer to ServiceNow.

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

• mini-imagenet (pre-processing)
• tiered-imagenet
• CUB

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

Pre-trained weights

https://zenodo.org/record/5552602#.YV2b-UbMKvU

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},
}