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
The PyTorch implementation of paper REST: Debiased Social Recommendation via Reconstructing Exposure Strategies

REST The PyTorch implementation of paper REST: Debiased Social Recommendation via Reconstructing Exposure Strategies. Usage Download dataset Download

DMIRLAB 2 Mar 13, 2022
ConvMAE: Masked Convolution Meets Masked Autoencoders

ConvMAE ConvMAE: Masked Convolution Meets Masked Autoencoders Peng Gao1, Teli Ma1, Hongsheng Li2, Jifeng Dai3, Yu Qiao1, 1 Shanghai AI Laboratory, 2 M

Alpha VL Team of Shanghai AI Lab 345 Jan 08, 2023
Unity Propagation in Bayesian Networks Handling Inconsistency via Unity Smoothing

This repository contains the scripts needed to generate the results from the paper Unity Propagation in Bayesian Networks Handling Inconsistency via U

0 Jan 19, 2022
Official code repository for the EMNLP 2021 paper

Integrating Visuospatial, Linguistic and Commonsense Structure into Story Visualization PyTorch code for the EMNLP 2021 paper "Integrating Visuospatia

Adyasha Maharana 23 Dec 19, 2022
Codes for AAAI 2022 paper: Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs

Context-Aware-Healthcare Codes for AAAI 2022 paper: Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs Download

LuChang 9 Dec 26, 2022
ISTR: End-to-End Instance Segmentation with Transformers (https://arxiv.org/abs/2105.00637)

This is the project page for the paper: ISTR: End-to-End Instance Segmentation via Transformers, Jie Hu, Liujuan Cao, Yao Lu, ShengChuan Zhang, Yan Wa

Jie Hu 182 Dec 19, 2022
An example project demonstrating how the Autonomous Learning Library can be used to build new reinforcement learning agents.

About This repository shows how Autonomous Learning Library can be used to build new reinforcement learning agents. In particular, it contains a model

Chris Nota 5 Aug 30, 2022
Jittor implementation of Recursive-NeRF: An Efficient and Dynamically Growing NeRF

Recursive-NeRF: An Efficient and Dynamically Growing NeRF This is a Jittor implementation of Recursive-NeRF: An Efficient and Dynamically Growing NeRF

33 Nov 30, 2022
Code of paper "Compositionally Generalizable 3D Structure Prediction"

Compositionally Generalizable 3D Structure Prediction In this work, We bring in the concept of compositional generalizability and factorizes the 3D sh

Songfang Han 30 Dec 17, 2022
Codes for the compilation and visualization examples to the HIF vegetation dataset

High-impedance vegetation fault dataset This repository contains the codes that compile the "Vegetation Conduction Ignition Test Report" data, which a

1 Dec 12, 2021
Audio-Visual Generalized Few-Shot Learning with Prototype-Based Co-Adaptation

Audio-Visual Generalized Few-Shot Learning with Prototype-Based Co-Adaptation The code repository for "Audio-Visual Generalized Few-Shot Learning with

Kaiaicy 3 Jun 27, 2022
Python/Rust implementations and notes from Proofs Arguments and Zero Knowledge

What is this? This is where I'll be collecting resources related to the Study Group on Dr. Justin Thaler's Proofs Arguments And Zero Knowledge Book. T

Thor 66 Jan 04, 2023
This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

haifeng xia 32 Oct 26, 2022
face2comics by Sxela (Alex Spirin) - face2comics datasets

This is a paired face to comics dataset, which can be used to train pix2pix or similar networks.

Alex 164 Nov 13, 2022
Learning with Subset Stacking

Learning with Subset Stacking (LESS) LESS is a new supervised learning algorithm that is based on training many local estimators on subsets of a given

S. Ilker Birbil 19 Oct 04, 2022
🇰🇷 Text to Image in Korean

KoDALLE Utilizing pretrained language model’s token embedding layer and position embedding layer as DALLE’s text encoder. Background Training DALLE mo

HappyFace 74 Sep 22, 2022
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation

RIFE RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation Ported from https://github.com/hzwer/arXiv2020-RIFE Dependencies NumPy

49 Jan 07, 2023
MinkLoc3D-SI: 3D LiDAR place recognition with sparse convolutions,spherical coordinates, and intensity

MinkLoc3D-SI: 3D LiDAR place recognition with sparse convolutions,spherical coordinates, and intensity Introduction The 3D LiDAR place recognition aim

16 Dec 08, 2022
An implementation of a sequence to sequence neural network using an encoder-decoder

Keras implementation of a sequence to sequence model for time series prediction using an encoder-decoder architecture. I created this post to share a

Luke Tonin 195 Dec 17, 2022
The final project of "Applying AI to EHR Data" of "AI for Healthcare" nanodegree - Udacity.

Patient Selection for Diabetes Drug Testing Project Overview EHR data is becoming a key source of real-world evidence (RWE) for the pharmaceutical ind

Omar Laham 1 Jan 14, 2022