Repository for the paper : Meta-FDMixup: Cross-Domain Few-Shot Learning Guided byLabeled Target Data

Related tags

Deep LearningMeta-FDMixup
Overview

1 Meta-FDMIxup

Repository for the paper :

Meta-FDMixup: Cross-Domain Few-Shot Learning Guided byLabeled Target Data. (ACM MM 2021)

paper

News! the representation video loaded in 2021/10/06 in Bilibili

News! the representation video loaded in 2021/10/10 in Youtube

image

If you have any questions, feel free to contact me. My email is [email protected].

2 setup and datasets

2.1 setup

A anaconda envs is recommended:

conda create --name py36 python=3.6
conda activate py36
conda install pytorch torchvision -c pytorch
pip3 install scipy>=1.3.2
pip3 install tensorboardX>=1.4
pip3 install h5py>=2.9.0

Then, git clone our repo:

git clone https://github.com/lovelyqian/Meta-FDMixup
cd Meta-FDMixup

2.2 datasets

Totally five datasets inculding miniImagenet, CUB, Cars, Places, and Plantae are used.

  1. Following FWT-repo to download and setup all datasets. (It can be done quickly)

  2. Remember to modify your own dataset dir in the 'options.py'

  3. Under our new setting, we randomly select $num_{target}$ labeled images from the target base set to form the auxiliary set. The splits we used are provided in 'Sources/'.

3 pretrained ckps

We provide several pretrained ckps.

You can download and put them in the 'output/pretrained_ckps/'

3.1 pretrained model trained on the miniImagenet

3.2 full model meta-trained on the target datasets

Since our method is target-set specific, we have to train a model for each target dataset.

Notably, as we stated in the paper, we use the last checkpoint for target dataset, while the best model on the validation set of miniImagenet is used for miniImagenet. Here, we provide the model of 'miniImagenet|CUB' as an example.

4 usage

4.1 network pretraining

python3 network_train.py --stage pretrain  --name pretrain-model --train_aug

If you have downloaded our pretrained_model_399.tar, you can just skip this step.

4.2 pretrained model testing

# test source dataset (miniImagenet)
python network_test.py --ckp_path output/checkpoints/pretrain-model/399.tar --stage pretrain --dataset miniImagenet --n_shot 5 

# test target dataset e.g. cub
python network_test.py --ckp_path output/checkpoints/pretrain-model/399.tar --stage pretrain --dataset cub --n_shot 5

you can test our pretrained_model_399.tar in the same way:

# test source dataset (miniImagenet)
python network_test.py --ckp_path output/pretrained_ckps/pretrained_model_399.tar --stage pretrain --dataset miniImagenet --n_shot 5 


# test target dataset e.g. cub
python network_test.py --ckp_path output/pretrained_ckps/pretrained_model_399.tar --stage pretrain --dataset cub --n_shot 5

4.3 network meta-training

# traget set: CUB
python3 network_train.py --stage metatrain --name metatrain-model-5shot-cub --train_aug --warmup output/checkpoints/pretrain-model/399.tar --target_set cub --n_shot 5

# target set: Cars
python3 network_train.py --stage metatrain --name metatrain-model-5shot-cars --train_aug --warmup output/checkpoints/pretrain-model/399.tar --target_set cars --n_shot 5

# target set: Places
python3 network_train.py --stage metatrain --name metatrain-model-5shot-places --train_aug --warmup output/checkpoints/pretrain-model/399.tar --target_set places --n_shot 5

# target set: Plantae
python3 network_train.py --stage metatrain --name metatrain-model-5shot-plantae --train_aug --warmup output/checkpoints/pretrain-model/399.tar --target_set plantae --n_shot 5

Also, you can use our pretrained_model_399.tar for warmup:

# traget set: CUB
python3 network_train.py --stage metatrain --name metatrain-model-5shot-cub --train_aug --warmup output/pretrained_ckps/pretrained_model_399.tar --target_set cub --n_shot 5

4.4 network testing

To test our provided full models:

# test target dataset (CUB)
python network_test.py --ckp_path output/pretrained_ckps/full_model_5shot_target_cub_399.tar --stage metatrain --dataset cub --n_shot 5 

# test target dataset (Cars)
python network_test.py --ckp_path output/pretrained_ckps/full_model_5shot_target_cars_399.tar --stage metatrain --dataset cars --n_shot 5 

# test target dataset (Places)
python network_test.py --ckp_path output/pretrained_ckps/full_model_5shot_target_places_399.tar --stage metatrain --dataset places --n_shot 5 

# test target dataset (Plantae)
python network_test.py --ckp_path output/pretrained_ckps/full_model_5shot_target_places_399.tar --stage metatrain --dataset plantae --n_shot 5 


# test source dataset (miniImagenet|CUB)
python network_test.py --ckp_path output/pretrained_ckps/full_model_5shot_target_cub_best_eval.tar --stage metatrain --dataset miniImagenet --n_shot 5

To test your models, just modify the 'ckp-path'.

5 citing

If you find our paper or this code useful for your research, please cite us:

@article{fu2021meta,
  title={Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data},
  author={Fu, Yuqian and Fu, Yanwei and Jiang, Yu-Gang},
  journal={arXiv preprint arXiv:2107.11978},
  year={2021}
}

6 Note

Notably, our code is built upon the implementation of FWT-repo.

Owner
Fu Yuqian
Fu Yuqian
GitHub Repository
Scalable training for dense retrieval models.

Scalable implementation of dense retrieval. Training on cluster By default it trains locally: PYTHONPATH=.:$PYTHONPATH python dpr_scale/main.py traine

Facebook Research 90 Dec 28, 2022
Neural Scene Flow Prior (NeurIPS 2021 spotlight)

Neural Scene Flow Prior Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey Will appear on Thirty-fifth Conference on Neural Information Processing Syste

Lilac Lee 85 Jan 03, 2023
Malware Analysis Neural Network project.

MalanaNeuralNetwork Description Malware Analysis Neural Network project. Table of Contents Getting Started Requirements Installation Clone Set-Up VENV

2 Nov 13, 2021
Distance Encoding for GNN Design

Distance-encoding for GNN design This repository is the official PyTorch implementation of the DEGNN and DEAGNN framework reported in the paper: Dista

172 Nov 08, 2022
Scales, Chords, and Cadences: Practical Music Theory for MIR Researchers

ISMIR-musicTheoryTutorial This repository has slides and Jupyter notebooks for the ISMIR 2021 tutorial Scales, Chords, and Cadences: Practical Music T

Johanna Devaney 58 Oct 11, 2022
Official PyTorch Implementation of paper "NeLF: Neural Light-transport Field for Single Portrait View Synthesis and Relighting", EGSR 2021.

NeLF: Neural Light-transport Field for Single Portrait View Synthesis and Relighting Official PyTorch Implementation of paper "NeLF: Neural Light-tran

Ken Lin 38 Dec 26, 2022
Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning And private Server services

Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning

MaCan 4.2k Dec 29, 2022
Dashboard for the COVID19 spread

COVID-19 Data Explorer App A streamlit Dashboard for the COVID-19 spread. The app is live at: [https://covid19.cwerner.ai]. New data is queried from G

Christian Werner 22 Sep 29, 2022
A PyTorch implementation of the continual learning experiments with deep neural networks

Brain-Inspired Replay A PyTorch implementation of the continual learning experiments with deep neural networks described in the following paper: Brain

182 Dec 27, 2022
Learning Time-Critical Responses for Interactive Character Control

Learning Time-Critical Responses for Interactive Character Control Abstract This code implements the paper Learning Time-Critical Responses for Intera

Movement Research Lab 227 Dec 31, 2022
Chinese Advertisement Board Identification(Pytorch)

Chinese-Advertisement-Board-Identification. We use YoloV5 to extract the ROI of the location of the chinese word. Next, we sort the bounding box and recognize every chinese words which we extracted.

Li-Wei Hsiao 12 Jul 21, 2022
Python Tensorflow 2 scripts for detecting objects of any class in an image without knowing their label.

Tensorflow-Mobile-Generic-Object-Localizer Python Tensorflow 2 scripts for detecting objects of any class in an image without knowing their label. Ori

Ibai Gorordo 11 Nov 15, 2022
3D-Reconstruction 基于深度学习方法的单目多视图三维重建

基于深度学习方法的单目多视图三维重建 Part I 三维重建 代码：Part1 技术文档：[Markdown] [PDF] 原始图像：Original Images 点云结果：Point Cloud Results-1

HMT_Curo 19 Dec 26, 2022
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

CNTK Chat Windows build status Linux build status The Microsoft Cognitive Toolkit (https://cntk.ai) is a unified deep learning toolkit that describes

Microsoft 17.3k Dec 29, 2022
Companion repo of the UCC 2021 paper "Predictive Auto-scaling with OpenStack Monasca"

Predictive Auto-scaling with OpenStack Monasca Giacomo Lanciano*, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella 2021 IEEE/ACM 14t

Giacomo Lanciano 0 Dec 07, 2022
Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity

Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity Indic TTS Samples can be found at https://peter-yh-wu.github.io/cross-

Peter Wu 1 Nov 12, 2022
Official implementation of the paper 'Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution' in CVPR 2022

LDL Paper | Supplementary Material Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution Jie Liang*, Hu

150 Dec 26, 2022
Modified fork of Xuebin Qin's U-2-Net Repository. Used for demonstration purposes.

U^2-Net (U square net) Modified version of U2Net used for demonstation purposes. Paper: U^2-Net: Going Deeper with Nested U-Structure for Salient Obje

Shreyas Bhat Kera 13 Aug 28, 2022
Dynamic Environments with Deformable Objects (DEDO)

DEDO - Dynamic Environments with Deformable Objects DEDO is a lightweight and customizable suite of environments with deformable objects. It is aimed

Rika 32 Dec 22, 2022
TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation Zhaoyun Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li

DamoCV 25 Dec 16, 2022