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DPGN: Distribution Propagation Graph Network for Few-shot Learning

This repository is the official implementation of DPGN: Distribution Propagation Graph Network for Few-shot Learning.

Abstract

Most graph-network-based meta-learning approaches model instance-level relation of examples. We extend this idea further to explicitly model the distribution-level relation of one example to all other examples in a 1-vs-N manner. We propose a novel approach named distribution propagation graph network (DPGN) for few-shot learning. It conveys both the distribution-level relations and instance-level relations in each few-shot learning task. To combine the distribution-level relations and instance-level relations for all examples, we construct a dual complete graph network which consists of a point graph and a distribution graph with each node standing for an example. Equipped with dual graph architecture, DPGN propagates label information from labeled examples to unlabeled examples within several update generations. In extensive experiments on few-shot learning benchmarks, DPGN outperforms state-of-the-art results by a large margin in 5% ∼ 12% under supervised settings and 7% ∼ 13% under semi-supervised settings.

Requirements

CUDA Version: 10.1

Python : 3.5.2

To install dependencies:

sudo pip3 install -r requirements.txt

Dataset

For your convenience, you can download the datasets directly from links on the left, or you can make them from scratch following the original splits on the right.

Dataset Original Split
Mini-ImageNet Matching Networks
Tiered-ImageNet SSL
CIFAR-FS R2D2
CUB-200-2011 Closer Look

The dataset directory should look like this:

├── dataset
    ├── mini-imagenet
        ├── mini_imagenet_test.pickle   
        ├── mini_imagenet_train.pickle  
        ├── mini_imagenet_val.pickle
    ├── tiered-imagenet
        ├── class_names.txt   
        ├── synsets.txt  
        ├── test_images.npz
        ├── test_labels.pkl   
        ├── train_images.npz  
        ├── train_labels.pkl
        ├── val_images.npz
        ├── val_labels.pkl
    ├── cifar-fs
        ├── cifar_fs_test.pickle   
        ├── cifar_fs_train.pickle  
        ├── cifar_fs_val.pickle
    ├── cub-200-2011
        ├── attributes   
        ├── bounding_boxes.txt 
        ├── classes.txt
        ├── image   
        ├── image_class_labels.txt 
        ├── images
        ├── images.txt   
        ├── parts
        ├── README
        ├── split
        ├── train_test_split.txt

Training

To train the model(s) in the paper, run:

python3 main.py --dataset_root dataset --config config/5way_1shot_resnet12_mini-imagenet.py --num_gpu 1 --mode train

Evaluation

To evaluate the model(s) in the paper, run:

python3 main.py --dataset_root dataset --config config/5way_1shot_resnet12_mini-imagenet.py --num_gpu 1 --mode eval

Pre-trained Models

This Google Drive contains pre-trained model under settings of 5way-1shot and 5way-5shots for mini-ImageNet dataset with ResNet12 backbone.

Results

# Default checkpoints directory is:
./checkpoints
# Default logs directory is:
./logs

Our model achieves the following performance on mini-ImageNet, tiered-ImageNet, CUB-200-2011 and CIFAR-FS (more detailed experimental results are in the paper).

miniImageNet:

Method Backbone 5way-1shot 5way-5shot
MatchingNet ConvNet 43.56±0.84 55.31± 0.73
ProtoNet ConvNet 49.42±0.78 68.20±0.66
RelationNet ConvNet 50.44±0.82 65.32±0.70
MAML ConvNet 48.70±1.84 55.31±0.73
GNN ConvNet 50.33±0.36 66.41±0.63
TPN ConvNet 55.51±0.86 69.86±0.65
Edge-label ConvNet 59.63±0.52 76.34±0.48
DPGN ConvNet 66.01±0.36 82.83±0.41
LEO WRN 61.76±0.08 77.59±0.12
wDAE WRN 61.07±0.15 76.75±0.11
DPGN WRN 67.24±0.51 83.72±0.44
CloserLook ResNet18 51.75±0.80 74.27±0.63
CTM ResNet18 62.05±0.55 78.63±0.06
DPGN ResNet18 66.63±0.51 84.07±0.42
MetaGAN ResNet12 52.71±0.64 68.63±0.67
SNAIL ResNet12 55.71±0.99 68.88±0.92
TADAM ResNet12 58.50±0.30 76.70±0.30
Shot-Free ResNet12 59.04±0.43 77.64±0.39
Meta-Transfer ResNet12 61.20±1.80 75.53±0.80
FEAT ResNet12 62.96±0.02 78.49±0.02
MetaOptNet ResNet12 62.64±0.61 78.63±0.46
DPGN ResNet12 67.77±0.32 84.60±0.43

tieredImageNet:

Method backbone 5way-1shot 5way-5shot
MAML ConvNet 51.67±1.81 70.30±1.75
ProtoNet ConvNet 53.34±0.89 72.69±0.74
RelationNet ConvNet 54.48±0.93 71.32±0.78
TPN ConvNet 59.91±0.94 73.30±0.75
Edge-label ConvNet 63.52±0.52 80.24±0.49
DPGN ConvNet 69.43±0.49 85.92±0.42
CTM ResNet18 64.78±0.11 81.05±0.52
DPGN ResNet18 70.46±0.52 86.44±0.41
TapNet ResNet12 63.08±0.15 80.26±0.12
Meta-Transfer ResNet12 65.62±1.80 80.61±0.90
MetaOptNet ResNet12 65.81±0.74 81.75±0.53
Shot-Free ResNet12 66.87±0.43 82.64±0.39
DPGN ResNet12 72.45±0.51 87.24±0.39

CUB-200-2011:

Method backbone 5way-1shot 5way-5shot
ProtoNet ConvNet 51.31±0.91 70.77±0.69
MAML ConvNet 55.92±0.95 72.09±0.76
MatchingNet ConvNet 61.16±0.89 72.86±0.70
RelationNet ConvNet 62.45±0.98 76.11±0.69
CloserLook ConvNet 60.53±0.83 79.34±0.61
DN4 ConvNet 53.15±0.84 81.90±0.60
DPGN ConvNet 76.05±0.51 89.08±0.38
FEAT ResNet12 68.87±0.22 82.90±0.15
DPGN ResNet12 75.71±0.47 91.48±0.33

CIFAR-FS:

Method backbone 5way-1shot 5way-5shot
ProtoNet ConvNet 55.5±0.7 72.0±0.6
MAML ConvNet 58.9±1.9 71.5±1.0
RelationNet ConvNet 55.0±1.0 69.3±0.8
R2D2 ConvNet 65.3±0.2 79.4±0.1
DPGN ConvNet 76.4±0.5 88.4±0.4
Shot-Free ResNet12 69.2±0.4 84.7±0.4
MetaOptNet ResNet12 72.0±0.7 84.2±0.5
DPGN ResNet12 77.9±0.5 90.2±0.4