Temporal Output Discrepancy for Active Learning

PyTorch implementation of Semi-Supervised Active Learning with Temporal Output Discrepancy, ICCV 2021.

Introduction

• We present a loss measurement Temporal Output Discrepancy (TOD) that estimates the loss of unlabeled samples by evaluating the distance of model outputs at different SGD steps.
• We theoretically demonstrate that TOD is a lower-bound of accumulated sample loss.
• An unlabeled data sampling strategy and a semi-supervised training scheme are developed for active learning based on TOD.

TOD Active Data Selection

Results

Requirements

numpy

torch >= 1.0.1

torchvision >= 0.2.1

Data Preparation

Download image classification datasets (e.g., Cifar-10, Cifar-100, SVHN, or Caltech101) and put them under ./data.

If you would like to try Caltech101 dataset, please download the pretrained ResNet-18 model and put it under ./.

Directory structure should be like:

 TOD
 |-- data
     |-- 101_ObjectCategories
         |-- accordion
         |-- airplanes
         |-- anchor
         |-- ...
     |-- cifar-10-batches-py
     |-- cifar-100-python
     |-- svhn
         |-- train_32x32.mat
         |-- test_32x32.mat
 |-- resnet18-5c106cde.pth
 |-- ...

Quick Start

Run TOD active learning experiment on Cifar-10:

 bash run.sh

Specify Datasets, Active Sampling Strategies, and Auxiliary Losses

./config/: The dataset configurations, active learning settings (trials and cycles), and neural network training settings can be found in ./config folder.

--sampling: We provide implementations of active data sampling strategies including random sampling, learning loss for active learning (LL4AL), and our TOD sampling. Use --sampling to specify a sampling strategy.

--auxiliary: We also provide implementations of auxiliary training losses including LL4AL and our COD loss. Use --auxiliary to specify an auxiliary loss.

Examples

Cifar-100 dataset, TOD sampling, no unsupervised loss:

 python main_TOD.py --config cifar100 --sampling TOD --auxiliary NONE

Caltech101 dataset, random sampling, COD loss:

 python main_TOD.py --config caltech101 --sampling RANDOM --auxiliary TOD

SVHN dataset, LL4AL sampling, LL4AL loss:

 python main_LL4AL.py --config svhn --sampling LL4AL --auxiliary LL4AL

Citation

 @inproceedings{huang2021semi,
  title={Semi-Supervised Active Learning with Temporal Output Discrepancy},
  author={Huang, Siyu and Wang, Tianyang and Xiong, Haoyi and Huan, Jun and Dou, Dejing},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2021}
 }

 @article{huang2022temporal,
  title={Temporal Output Discrepancy for Loss Estimation-Based Active Learning},
  author={Huang, Siyu and Wang, Tianyang and Xiong, Haoyi and Wen, Bihan and Huan, Jun and Dou, Dejing},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2022},
 }

Contact

Siyu Huang

huangsiyutc@gmail.com