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inc-few-shot-attractor-public

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]

Dependencies

• cv2
• numpy
• pandas
• python 2.7 / 3.5+
• tensorflow 1.11
• tqdm

Our code is tested on Ubuntu 14.04 and 16.04.

Setup

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.

miniImageNet

[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

tieredImageNet

[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

Config files

Run make to make protobuf files.

git clone https://github.com/renmengye/inc-few-shot-attractor.git
cd inc-few-shot-attractor
make

Core Experiments

Pretraining

./run.sh {GPUID} python run_exp.py --config {CONFIG_FILE}     \
                  --dataset {DATASET}                         \
                  --data_folder {DATASET_FOLDER}              \
                  --results {SAVE_FOLDER}                     \
                  --tag {EXPERIMENT_NAME}

• Possible DATASET options are mini-imagenet, tiered-imagenet.
• Possible CONFIG options are any prototxt file in the ./configs/pretrain folder.

Meta-learning

./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 DATASET options are mini-imagenet, tiered-imagenet.
• Possible CONFIG options are any prototxt file in the ./configs/attractors folder, e.g. \*-{mlp|lr}-attn-s{1|5}.prototxt means 1/5-shot model using MLP or LR as fast weights model.
• You need to pass in PRETRAIN_CKPT_FOLDER option with the pretrained model.
• Add --retest flag for restoring a fully trained model and re-run eval.

Baselines

• Baseline configs are in ./configs/lwof and ./configs/imprint.
• For ProtoNet baseline please run run_proto_exp.py with the same flags from the previous section.
• Configs for ablation studies can be found in ./configs/ablation.

Citation

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},
}