Label Hallucination for Few-Shot Classification

Related tags

Deep LearningLabelHalluc
Overview

Label Hallucination for Few-Shot Classification

This repo covers the implementation of the following paper: Label Hallucination for Few-Shot Classification . If you find this repo useful for your research, please consider citing the paper.

@article{Jian2022LabelHalluc,
    author = {Yiren Jian and Lorenzo Torresani},
    title = {Label Hallucination for Few-shot Classification},
    journal = {AAAI},
    year = {2022}
}
@article{jian2021label,
      title={Label Hallucination for Few-Shot Classification},
      author={Yiren Jian and Lorenzo Torresani},
      journal={arXiv preprint arXiv:2112.03340},
      year={2021}
}

Requirements

This repo was tested with Ubuntu 18.04.5 LTS, Python 3.6, PyTorch 1.4.0, and CUDA 10.1. You will need at least 32GB RAM and 22GB VRAM (i.e. two Nvidia RTX-2080Ti) for running full experiments in this repo.

Download Data

The data we used here is preprocessed by the repo of MetaOptNet, Please find the renamed versions of the files in below link by RFS.

Download and unzip the dataset, put them under data directory.

Embedding Learning

Please follow RFS, SKD and Rizve et al. (or other transfer learning methods) for the embedding learning. RFS provides a Dropbox link for downloading their pre-trained models for miniImageNet.

We provide our pretrained embedding models by [SKD] and [Rizve et al.] at Dropbox. Note that those models are NOT the official release by original authors, and they perform slightly worse than what reported in their papers. Better models could be trained with longer durations and/or by hyper-parameters tuning.

Once finish the embedding training, put the pre-trained models in models_pretrained directory.

Running Our Fine-tuning

To perform 5-way 5-shot classifications, run:

# For CIFAR-FS
CUDA_VISIBLE_DEVICES=0 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset CIFAR-FS --data_root data/CIFAR-FS/ --model_path models_pretrained/cifar-fs_skd_gen1.pth --n_shot 5 --n_aug_support 5 --epoch 1 --norm_feat

# For FC100
CUDA_VISIBLE_DEVICES=0 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset FC100 --data_root data/FC100/ --model_path models_pretrained/fc100_skd_gen1.pth --n_shot 5 --n_aug_support 5 --epoch 1 --norm_feat

# For miniImageNet (require multiple GPUs)
CUDA_VISIBLE_DEVICES=0,1 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset miniImageNet --data_root data/miniImageNet/ --model_path models_pretrained/mini_skd_gen1.pth --n_shot 5 --n_aug_support 5 --epoch 1 --norm_feat

# For tieredImageNet (require multiple GPUs)
CUDA_VISIBLE_DEVICES=0,1 python -W ignore eval_fewshot_SoftPseudoLabel_tieredImageNet.py --dataset tieredImageNet --data_root data/tieredImageNet/ --model_path models_pretrained/tiered_skd_gen0.pth --n_shot 5 --n_aug_support 5  --early 200 --print 50 --norm_feat

To perform 5-way 1-shot classifications, run:

# For CIFAR-FS
CUDA_VISIBLE_DEVICES=0 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset CIFAR-FS --data_root data/CIFAR-FS/ --model_path models_pretrained/cifar-fs_skd_gen1.pth --n_shot 1 --n_aug_support 25 --epoch 3 --norm_feat

# For FC100
CUDA_VISIBLE_DEVICES=0 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset FC100 --data_root data/FC100/ --model_path models_pretrained/fc100_skd_gen1.pth --n_shot 1 --n_aug_support 25 --epoch 5 --norm_feat

# For miniImageNet (require multiple GPUs)
CUDA_VISIBLE_DEVICES=0,1 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset miniImageNet --data_root data/miniImageNet/ --model_path models_pretrained/mini_skd_gen1.pth --n_shot 1 --n_aug_support 25 --early 150 --norm_feat

# For tieredImageNet (require multiple GPUs)
CUDA_VISIBLE_DEVICES=0,1 python -W ignore eval_fewshot_SoftPseudoLabel_tieredImageNet.py --dataset tieredImageNet --data_root data/tieredImageNet/ --model_path models_pretrained/tiered_skd_gen0.pth --n_shot 1 --n_aug_support 25  --early 200 --print 50 --norm_feat

Reading the outputs

400it RFS/SKD/baseline acc: 0.7200 for this episode
==> training...
Epoch: [1][100/288]    Time 0.121 (0.115)    Data 0.001 (0.003)    ..
Epoch: [1][200/288]    Time 0.112 (0.114)    Data 0.001 (0.002)    ...
epoch 400, total time 32.77
acc1: 0.6567, std1: 0.0076, acc2: 0.6820, std2: 0.0080,
epochs: 1, acc2: 0.6400, std2: 0.0080
...

The above is an example print-out for FC100 5-shot. acc1: 0.6567, std1: 0.0076 is the accuracy and the deviation of LinearRegression method with fixed embeddings (used in RFS and SKD). acc2: 0.6820, std2: 0.0080 is the result by our method.

Contacts

For any questions, please contact authors.

Acknowlegements

Thanks to RFS, for the preliminary implementations.

Owner
Yiren Jian
PhD student in Computer Vision and NLP
Yiren Jian
GitHub Repository
Research code for the paper "Variational Gibbs inference for statistical estimation from incomplete data".

Variational Gibbs inference (VGI) This repository contains the research code for Simkus, V., Rhodes, B., Gutmann, M. U., 2021. Variational Gibbs infer

Vaidotas Šimkus 1 Apr 08, 2022
[ICLR'21] FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

FedBN: Federated Learning on Non-IID Features via Local Batch Normalization This is the PyTorch implemention of our paper FedBN: Federated Learning on

<a href=[email protected]"> 156 Dec 15, 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
ICLR2021 (Under Review)

Self-Supervised Time Series Representation Learning by Inter-Intra Relational Reasoning This repository contains the official PyTorch implementation o

Haoyi Fan 58 Dec 30, 2022
JAX code for the paper "Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation"

Optimal Model Design for Reinforcement Learning This repository contains JAX code for the paper Control-Oriented Model-Based Reinforcement Learning wi

Evgenii Nikishin 43 Sep 28, 2022
Pytorch code for paper "Image Compressed Sensing Using Non-local Neural Network" TMM 2021.

NL-CSNet-Pytorch Pytorch code for paper "Image Compressed Sensing Using Non-local Neural Network" TMM 2021. Note: this repo only shows the strategy of

WenxueCui 7 Nov 07, 2022
Implementation for On Provable Benefits of Depth in Training Graph Convolutional Networks

Implementation for On Provable Benefits of Depth in Training Graph Convolutional Networks Setup This implementation is based on PyTorch = 1.0.0. Smal

Weilin Cong 8 Oct 28, 2022
Tools for manipulating UVs in the Blender viewport.

UV Tool Suite for Blender A set of tools to make editing UVs easier in Blender. These tools can be accessed wither through the Kitfox - UV panel on th

35 Oct 29, 2022
Repo for code associated with Modeling the Mitral Valve.

Project Title Mitral Valve Getting Started Repo for code associated with Modeling the Mitral Valve. See https://arxiv.org/abs/1902.00018 for preprint,

Alex Kaiser 1 May 17, 2022
Rust bindings for the C++ api of PyTorch.

tch-rs Rust bindings for the C++ api of PyTorch. The goal of the tch crate is to provide some thin wrappers around the C++ PyTorch api (a.k.a. libtorc

Laurent Mazare 2.3k Dec 30, 2022
The PyTorch improved version of TPAMI 2017 paper: Face Alignment in Full Pose Range: A 3D Total Solution.

Face Alignment in Full Pose Range: A 3D Total Solution By Jianzhu Guo. [Updates] 2020.8.30: The pre-trained model and code of ECCV-20 are made public

Jianzhu Guo 3.4k Jan 02, 2023
Universal Adversarial Triggers for Attacking and Analyzing NLP (EMNLP 2019)

Universal Adversarial Triggers for Attacking and Analyzing NLP This is the official code for the EMNLP 2019 paper, Universal Adversarial Triggers for

Eric Wallace 248 Dec 17, 2022
Manage the availability of workspaces within Frappe/ ERPNext (sidebar) based on user-roles

Workspace Permissions Manage the availability of workspaces within Frappe/ ERPNext (sidebar) based on user-roles. Features Configure foreach workspace

Patrick.St. 18 Sep 26, 2022
DrQ-v2: Improved Data-Augmented Reinforcement Learning

DrQ-v2: Improved Data-Augmented RL Agent Method DrQ-v2 is a model-free off-policy algorithm for image-based continuous control. DrQ-v2 builds on DrQ,

Facebook Research 234 Jan 01, 2023
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

Mask R-CNN for Object Detection and Segmentation This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bound

Matterport, Inc 22.5k Jan 04, 2023
Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).

Densely Connected Convolutional Networks (DenseNets) This repository contains the code for DenseNet introduced in the following paper Densely Connecte

Zhuang Liu 4.5k Jan 03, 2023
View model summaries in PyTorch!

torchinfo (formerly torch-summary) Torchinfo provides information complementary to what is provided by print(your_model) in PyTorch, similar to Tensor

Tyler Yep 1.5k Jan 05, 2023
SPTAG: A library for fast approximate nearest neighbor search

SPTAG: A library for fast approximate nearest neighbor search SPTAG SPTAG (Space Partition Tree And Graph) is a library for large scale vector approxi

Microsoft 4.3k Jan 01, 2023
Implementation of Neonatal Seizure Detection using EEG signals for deploying on edge devices including Raspberry Pi.

NeonatalSeizureDetection Description Link: https://arxiv.org/abs/2111.15569 Citation: @misc{nagarajan2021scalable, title={Scalable Machine Learn

Vishal Nagarajan 11 Nov 08, 2022
Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (ICLR 2020)

U-GAT-IT — Official TensorFlow Implementation (ICLR 2020) : Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization fo

Junho Kim 6.2k Jan 04, 2023