Distance-Ratio-Based Formulation for Metric Learning

Environment

• Python3
• Pytorch (http://pytorch.org/) (version 1.6.0+cu101)
• json
• tqdm

Preparing datasets

CUB

• Change directory to /filelists/CUB
• run source ./download_CUB.sh

One might need to manually download CUB data from http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz.

mini-ImageNet

• Change directory to /filelists/miniImagenet
• run source ./download_miniImagenet.sh (WARNING: This would download the 155G ImageNet dataset.)

To only download 'miniImageNet dataset' and not the whole 155G ImageNet dataset:

(Download 'csv' files from the codes in /filelists/miniImagenet/download_miniImagenet.sh. Then, do the following.)

First, download zip file from https://drive.google.com/file/d/0B3Irx3uQNoBMQ1FlNXJsZUdYWEE/view (It is from https://github.com/oscarknagg/few-shot). After unzipping the zip file at /filelists/miniImagenet, run a script /filelists/miniImagenet/prepare_mini_imagenet.py which is modified from https://github.com/oscarknagg/few-shot/blob/master/scripts/prepare_mini_imagenet.py. Then, run /filelists/miniImagenet/write_miniImagenet_filelist2.py.

Train

Run python ./train.py --dataset [DATASETNAME] --model [BACKBONENAME] --method [METHODNAME] --train_aug [--OPTIONARG]

To also save training analyses results, for example, run python ./train.py --dataset miniImagenet --model Conv4 --method protonet_S --train_aug --n_shot 5 --train_n_way 5 --test_n_way 5 > record/miniImagenet_Conv4_proto_S_5s5w.txt

train_models.ipynb contains codes for our experiments.

Save features

Save the extracted feature before the classifaction layer to increase test speed.

For instance, run python ./save_features.py --dataset miniImagenet --model Conv4 --method protonet_S --train_aug --n_shot 5 --train_n_way 5

Test

For example, run python ./test.py --dataset miniImagenet --model Conv4 --method protonet_S --train_aug --n_shot 5 --train_n_way 5 --test_n_way 5

Analyze training

Run /record/analyze_training_1shot.ipynb and /record/analyze_training_5shot.ipynb to analyze training results (norm ratio, con-alpha ratio, div-alpha ratio, and con-div ratio)

Results

The test results will be recorded in ./record/results.txt

Visual comparison of softmax-based and distance-ratio-based (DR) formulation

The following images visualize confidence scores of red class when the three points are the representing points of red, green, and blue classes.

Softmax-based formulation	DR formulation

References and licence

Our repository (a set of codes) is forked from an original repository (https://github.com/wyharveychen/CloserLookFewShot) and codes are under the same licence (LICENSE.txt) as the original repository except for the following.

/filelists/miniImagenet/prepare_mini_imagenet.py file is modifed from https://github.com/oscarknagg/few-shot. It is under a different licence in /filelists/miniImagenet/prepare_mini_imagenet.LICENSE

Copyright and licence notes (including the copyright note in /data/additional_transforms.py) are from the original repositories (https://github.com/wyharveychen/CloserLookFewShot and https://github.com/oscarknagg/few-shot).

Modifications

List of modified or added files (or folders) compared to the original repository (https://github.com/wyharveychen/CloserLookFewShot):

io_utils.py backbone.py configs.py train.py save_features.py test.py utils.py README.md train_models.ipynb /methods/__init__.py /methods/protonet_S.py /methods/meta_template.py /methods/protonet_DR.py /methods/softmax_1nn.py /methods/DR_1nn.py /models/ /filelists/miniImagenet/prepare_mini_imagenet.py /filelists/miniImagenet/prepare_mini_imagenet.LICENSE /filelists/miniImagenet/write_miniImagenet_filelist2.py /record/ /record/preprocessed/ /record/analyze_training_1shot.ipynb /record/analyze_training_5shot.ipynb

My (Hyeongji Kim) main contributions (modifications) are in /methods/meta_template.py, /methods/protonet_DR.py, /methods/softmax_1nn.py, /methods/DR_1nn.py, /record/analyze_training_1shot.ipynb, and /record/analyze_training_5shot.ipynb.