uestcwcw / meta-open

Meta-Open

This repository contains code for the paper:

Few-Shot Open-Set Recognition with Meta-Learning [PDF]

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020

Abstract

The problem of open-set recognition is considered. While previous approaches only consider this problem in the context of large-scale classifier training, we seek a unified solution for this and the low-shot classification setting. It is argued that the classic softmax classifier is a poor solution for open-set recognition, since it tends to overfit on the training classes. Randomization is then proposed as a solution to this problem. This suggests the use of meta-learning techniques, commonly used for few-shot classification, for the solution of open-set recognition. A new oPen sEt mEta LEaRning (PEELER) algorithm is then introduced. This combines the random selection of a set of novel classes per episode, a loss that maximizes the posterior entropy for examples of those classes, and a new metric learning formulation based on the Mahalanobis distance. Experimental results show that PEELER achieves state of the art open set recognition performance for both few-shot and large-scale recognition. On CIFAR and miniImageNet, it achieves substantial gains in seen/unseen class detection AUROC for a given seen-class classification accuracy.

 

 

If you find this code useful, consider citing our work:

@inproceedings{liu2020few,
  title={Few-Shot Open-Set Recognition using Meta-Learning},
  author={Liu, Bo and Kang, Hao and Li, Haoxiang and Hua, Gang and Vasconcelos, Nuno},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={8798--8807},
  year={2020}
}

Requirements

• Python 3
• PyTorch (version >= 0.4.1)
• yaml

Dataset

• mini-ImageNet
  • Download the dataset following here (from https://github.com/gidariss/FewShotWithoutForgetting).
  • Put the gzip file under ./dataset/miniImageNet
  • cd dataset/miniImageNet
  • sh prepare.sh

Training and Evaluating

1. Training

python main.py --cfg ./config/openfew/default.yaml

2. Testing

python main.py --cfg ./config/openfew/default.yaml --test

Results and Models

Few-Shot

Setting	Accuracy	AUROC	Model
5-way 1-shot	57.90	62.05	ResNet

Model usage: unzip and move the entire directory under ./output