This repository contains code for the following paper: Incremental Few-Shot Learning with Attention Attractor Networks. Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel. NeurIPS 2019. [arxiv]
- cv2
- numpy
- pandas
- python 2.7 / 3.5+
- tensorflow 1.11
- tqdm
Our code is tested on Ubuntu 14.04 and 16.04.
First, designate a folder to be your data root:
export DATA_ROOT={DATA_ROOT}
Then, set up the datasets following the instructions in the subsections.
[Google Drive] (5GB)
# Download and place "mini-imagenet.tar.gz" in "$DATA_ROOT/mini-imagenet".
mkdir -p $DATA_ROOT/mini-imagenet
cd $DATA_ROOT/mini-imagenet
mv ~/Downloads/mini-imagenet.tar .
tar -xvf mini-imagenet.tar
rm -f mini-imagenet.tar
[Google Drive] (15GB)
# Download and place "tiered-imagenet.tar" in "$DATA_ROOT/tiered-imagenet".
mkdir -p $DATA_ROOT/tiered-imagenet
cd $DATA_ROOT/tiered-imagenet
mv ~/Downloads/tiered-imagenet.tar .
tar -xvf tiered-imagenet.tar
rm -f tiered-imagenet.tar
Note: Please make sure that the following hardware requirements are met before running tieredImageNet experiments.
- Disk: 30 GB
- RAM: 32 GB
Run make to make protobuf files.
git clone https://github.com/renmengye/inc-few-shot-attractor.git
cd inc-few-shot-attractor
make
./run.sh {GPUID} python run_exp.py --config {CONFIG_FILE} \
--dataset {DATASET} \
--data_folder {DATASET_FOLDER} \
--results {SAVE_FOLDER} \
--tag {EXPERIMENT_NAME}
- Possible
DATASEToptions aremini-imagenet,tiered-imagenet. - Possible
CONFIGoptions are any prototxt file in the./configs/pretrainfolder.
./run.sh {GPUID} python run_exp.py --config {CONFIG_FILE} \
--dataset {DATASET} \
--data_folder {DATASET_FOLDER} \
--pretrain {PRETRAIN_CKPT_FOLDER} \
--nshot {NUMBER_OF_SHOTS} \
--nclasses_b {NUMBER_OF_FEWSHOT_WAYS} \
--results {SAVE_FOLDER} \
--tag {EXPERIMENT_NAME} \
[--eval] \
[--retest]
- Possible
DATASEToptions aremini-imagenet,tiered-imagenet. - Possible
CONFIGoptions are any prototxt file in the./configs/attractorsfolder, e.g.\*-{mlp|lr}-attn-s{1|5}.prototxtmeans 1/5-shot model using MLP or LR as fast weights model. - You need to pass in
PRETRAIN_CKPT_FOLDERoption with the pretrained model. - Add
--retestflag for restoring a fully trained model and re-run eval.
- Baseline configs are in
./configs/lwofand./configs/imprint. - For ProtoNet baseline please run
run_proto_exp.pywith the same flags from the previous section. - Configs for ablation studies can be found in
./configs/ablation.
If you use our code, please consider cite the following:
- Mengye Ren, Renjie Liao, Ethan Fetaya and Richard S. Zemel. Incremental Few-Shot Learning with Attention Attractor Networks. In Advances in Neural Information Processing Systems (NeurIPS), 2019.
@inproceedings{ren19incfewshot,
author = {Mengye Ren and
Renjie Liao and
Ethan Fetaya and
Richard S. Zemel},
title = {Incremental Few-Shot Learning with Attention Attractor Networks,
booktitle= {Advances in Neural Information Processing Systems (NeurIPS)},
year = {2019},
}