Activating discriminative semantic for few-shot classification

General image classification

Environment

This code requires Pytorch 1.10.0 and torchvision 0.11.0 or higher with cuda support.

---

Result

mini-ImageNet

Model	5-way 1-shot	5-way 5-shot
ProtoNet	62.39±0.21	80.53±0.14
Poodle*	74.21	83.71
Our	65.39±0.61	81.28±0.43
Our*	76.82±0.41	82.93±0.38

tiered-ImageNet

Model	5-way 1-shot	5-way 5-shot
ProtoNet	68.23±0.23	84.03±0.16
Poodle*	78.72	86.57
Our	70.03±0.72	84.53±0.49
Our*	84.41±0.40	86.15±0.36

---

Dataset

Download mini-ImageNet, tiered-ImageNet and CUB dataset and put them into ./ .

• mini-ImageNet
• tiered-ImageNet
• CUB

---

Run

For example, to train the 1-shot/5-shot 5-way model with ProtoNet backbone on MiniImageNet: python ./Mini_imageNet/pre_train_improve_k.py

to train the 1-shot/5-shot 5-way model with ProtoNet backbone on tieredImageNet: python ./Tiered_imagenet/pre_train_improve_k.py