XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning

This repository contains the code for the following ICML 2020 paper:

XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning

Dependencies

This code was tested on the following environment:

• Ubuntu 16.04
• python 3.6
• cv2
• numpy
• pandas
• tensorflow==1.11
• tensorflow-gpu==1.9.0 (If cuda is available)
• tqdm

Dataset

miniImageNet

Download and decompress the file: [miniImageNet] (courtesy of Mengye Ren)

export DATA_ROOT={path/to/dataset}
mkdir -p $DATA_ROOT
cd $DATA_ROOT
mv ~/Downloads/mini-imagenet.tar .   # place "mini-imagenet.tar" in $DATA_ROOT
tar -xvf mini-imagenet.tar
rm -f mini-imagenet.tar

tieredImageNet

Download and decompress the file: [tieredImageNet] (courtesy of Mengye Ren)

export DATA_ROOT={path/to/dataset}
mkdir -p $DATA_ROOT
cd $DATA_ROOT
mv ~/Downloads/tiered-imagenet.tar .   # place "tiered-imagenet.tar" in $DATA_ROOT
tar -xvf tiered-imagenet.tar
rm -f tiered-imagenet.tar

Usage

Clone this repository

https://github.com/EdwinKim3069/XtarNet.git
cd XtarNet

Pretraining

In order to pretrain backbone, run the python file run_pretrain.py.

For miniImageNet experiment,

import os
os.system('./run.sh 0 python run_exp.py '
          '--config configs/pretrain/mini-imagenet-resnet-snail.prototxt '
          '--dataset mini-imagenet '
          '--data_folder DATA_ROOT/mini-imagenet/ '
          '--results PATH/TO/SAVE_PRETRAIN_RESULTS '
          '--tag PATH/TO/EXP_TAG '
          )

For tieredImageNet experiment,

import os
os.system('./run.sh 0 python run_exp.py '
          '--config configs/pretrain/tiered-imagenet-resnet-18.prototxt '
          '--dataset tiered-imagenet '
          '--data_folder DATA_ROOT/tiered-imagenet/ '
          '--results PATH/TO/SAVE_PRETRAIN_RESULTS '
          '--tag PATH/TO/EXP_TAG '
          )

Meta-training

In order to run meta-training experiments, run the python file run_inc.py.

For miniImageNet experiments,

import os

nshot = 'NUMBER_OF_SHOTS'
tag = 'XtarNet_miniImageNet_{}shot'.format(nshot)
os.system('./run.sh 0 '
          'python run_exp.py '
          '--config configs/XtarNet/XtarNet-mini-imagenet-resnet-snail.prototxt '
          '--dataset mini-imagenet '
          '--data_folder DATA_ROOT/mini-imagenet/ '
          '--pretrain PATH/TO/PRETRAIN_RESULTS/TAG '
          '--nshot {} '
          '--nclasses_b 5 '
          '--results PATH/TO/SAVE_METATRAIN_RESULTS '
          # '--eval '
          # '--retest '
          '--tag {} '.format(nshot, tag)
          )

For tieredImageNet experiments,

import os

nshot = 'NUMBER_OF_SHOTS'
tag = 'XtarNet_tieredImageNet_{}shot'.format(nshot)
os.system('./run.sh 0 '
          'python run_exp.py '
          '--config configs/XtarNet/XtarNet-tiered-imagenet-resnet-18.prototxt '
          '--dataset tiered-imagenet '
          '--data_folder DATA_ROOT/tiered-imagenet/ '
          '--pretrain PATH/TO/PRETRAIN_RESULTS/TAG '
          '--nshot {} '
          '--nclasses_b 5 '
          '--results PATH/TO/SAVE_METATRAIN_RESULTS '
          # '--eval '
          # '--retest '
          '--tag {} '.format(nshot, tag)
          )

If you want to evaluate the meta-trained model, add --eval and --retest flag for restoring a fully trained model and re-run eval.

Acknowledgment

Our code is based on the implementations of Incremental Few-Shot Learning with Attention Attractor Networks.