EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients.

Related tags

Deep Learningeasy
Overview

EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients.

This repository is the official implementation of EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients.

EASY proposes a simple methodology, that reaches or even beats state of the art performance on multiple standardized benchmarks of the field, while adding almost no hyperparameters or parameters to those used for training the initial deep learning models on the generic dataset.

Downloads

Please click the Google Drive link for downloading the features, backbones and datasets.

Each of the files (backbones and features) have the following prefixes depending on the backbone:

Backbone prefix Number of parameters
ResNet12 12M
ResNet12(1/sqrt(2)) small 6M
ResNet12(1/2) tiny 3M

Each of the features file is named as follow :

  • if not AS : " features .pt11"
  • if AS : " featuresAS .pt11"

Testing scripts for EASY

Run scripts to evaluate the features on FSL tasks for Y and ASY. For EY and EASY use the corresponding features.

Inductive setup using NCM

Test features on miniimagenet using Y (Resnet12)

" --dataset miniimagenet --model resnet12 --test-features ' /minifeatures1.pt11' --preprocessing ME"> 
$ python main.py --dataset-path "
     
      " --dataset miniimagenet --model resnet12 --test-features '
      
       /minifeatures1.pt11' --preprocessing ME

Test features on miniimagenet using ASY (Resnet12)

" --dataset miniimagenet --model resnet12 --test-features ' /minifeaturesAS1.pt11' --preprocessing ME"> 
$ python main.py --dataset-path "
     
      " --dataset miniimagenet --model resnet12 --test-features '
      
       /minifeaturesAS1.pt11' --preprocessing ME

Test features on miniimagenet using EY (3xResNet12)

" --dataset miniimagenet --model resnet12 --test-features "[ /minifeatures1.pt11, /minifeatures2.pt11, /minifeatures3.pt11]" --preprocessing ME"> 
$ python main.py --dataset-path "
       
        " --dataset miniimagenet --model resnet12 --test-features "[
        
         /minifeatures1.pt11, 
         
          /minifeatures2.pt11, 
          
           /minifeatures3.pt11]" --preprocessing ME

Test features on miniimagenet using EASY (3xResNet12)

" --dataset miniimagenet --model resnet12 --test-features "[ /minifeaturesAS1.pt11, /minifeaturesAS2.pt11, /minifeaturesAS3.pt11]" --preprocessing ME "> 
$ python main.py --dataset-path "
       
        " --dataset miniimagenet --model resnet12 --test-features "[
        
         /minifeaturesAS1.pt11, 
         
          /minifeaturesAS2.pt11, 
          
           /minifeaturesAS3.pt11]" --preprocessing ME

Transductive setup using Soft k-means

Test features on miniimagenet using Y (ResNet12)

" --dataset miniimagenet --model resnet12 --test-features ' /minifeatures1.pt11'--postprocessing ME --transductive --transductive-softkmeans --transductive-temperature-softkmeans 20"> 
$ python main.py --dataset-path "
     
      " --dataset miniimagenet --model resnet12 --test-features '
      
       /minifeatures1.pt11'--postprocessing ME --transductive --transductive-softkmeans --transductive-temperature-softkmeans 20

Test features on miniimagenet using ASY (ResNet12)

" --dataset miniimagenet --model resnet12 --test-features ' /minifeaturesAS1.pt11' --postprocessing ME --transductive --transductive-softkmeans --transductive-temperature-softkmeans 20"> 
$ python main.py --dataset-path "
     
      " --dataset miniimagenet --model resnet12 --test-features '
      
       /minifeaturesAS1.pt11' --postprocessing ME --transductive --transductive-softkmeans --transductive-temperature-softkmeans 20

Test features on miniimagenet using EY (3xResNet12)

" --dataset miniimagenet --model resnet12 --test-features "[ /minifeatures1.pt11, /minifeatures2.pt11, /minifeatures3.pt11]" --postrocessing ME --transductive --transductive-softkmeans --transductive-temperature-softkmeans 20"> 
$ python main.py --dataset-path "
       
        " --dataset miniimagenet --model resnet12 --test-features "[
        
         /minifeatures1.pt11, 
         
          /minifeatures2.pt11, 
          
           /minifeatures3.pt11]" --postrocessing ME  --transductive --transductive-softkmeans --transductive-temperature-softkmeans 20

Test features on miniimagenet using EASY (3xResNet12)

" --dataset miniimagenet --model resnet12 --test-features "[ /minifeaturesAS1.pt11, /minifeaturesAS2.pt11, /minifeaturesAS3.pt11]" --postrocessing ME --transductive --transductive-softkmeans --transductive-temperature-softkmeans 20"> 
$ python main.py --dataset-path "
       
        " --dataset miniimagenet --model resnet12 --test-features "[
        
         /minifeaturesAS1.pt11, 
         
          /minifeaturesAS2.pt11, 
          
           /minifeaturesAS3.pt11]" --postrocessing ME  --transductive --transductive-softkmeans --transductive-temperature-softkmeans 20

Training scripts for Y

Train a model on miniimagenet using manifold mixup, self-supervision and cosine scheduler

" --dataset miniimagenet --model resnet12 --epochs 0 --manifold-mixup 500 --rotations --cosine --gamma 0.9 --milestones 100 --batch-size 128 --preprocessing ME "> 
$ python main.py --dataset-path "
    
     " --dataset miniimagenet --model resnet12 --epochs 0 --manifold-mixup 500 --rotations --cosine --gamma 0.9 --milestones 100 --batch-size 128 --preprocessing ME

Important Arguments

Some important arguments for our code.

Training arguments

  • dataset: choices=['miniimagenet', 'cubfs','tieredimagenet', 'fc100', 'cifarfs']
  • model: choices=['resnet12', 'resnet18', 'resnet20', 'wideresnet', 's2m2r']
  • dataset-path: path of the datasets folder which contains folders of all the datasets.

Few-shot Classification

  • preprocessing: preprocessing sequence for few shot given as a string, can contain R:relu P:sqrt E:sphering and M:centering using the base data.
  • postprocessing: postprocessing sequence for few shot given as a string, can contain R:relu P:sqrt E:sphering and M:centering on the few-shot data, used for transductive setting.

Few-shot classification Results

Experimental results on few-shot learning datasets with ResNet-12 backbone. We report our average results with 10000 randomly sampled episodes for both 1-shot and 5-shot evaluations.

MiniImageNet Dataset (inductive)

Methods 1-Shot 5-Way 5-Shot 5-Way
SimpleShot [29] 62.85 ± 0.20 80.02 ± 0.14
Baseline++ [30] 53.97 ± 0.79 75.90 ± 0.61
TADAM [35] 58.50 ± 0.30 76.70 ± 0.30
ProtoNet [10] 60.37 ± 0.83 78.02 ± 0.57
R2-D2 (+ens) [20] 64.79 ± 0.45 81.08 ± 0.32
FEAT [36] 66.78 82.05
CNL [37] 67.96 ± 0.98 83.36 ± 0.51
MERL [38] 67.40 ± 0.43 83.40 ± 0.28
Deep EMD v2 [13] 68.77 ± 0.29 84.13 ± 0.53
PAL [8] 69.37 ± 0.64 84.40 ± 0.44
inv-equ [39] 67.28 ± 0.80 84.78 ± 0.50
CSEI [40] 68.94 ± 0.28 85.07 ± 0.50
COSOC [9] 69.28 ± 0.49 85.16 ± 0.42
EASY 2×ResNet12 1/√2 (ours) 70.63 ± 0.20 86.28 ± 0.12
above <=12M nb of parameters below 36M
3S2M2R [12] 64.93 ± 0.18 83.18 ± 0.11
LR + DC [17] 68.55 ± 0.55 82.88 ± 0.42
EASY 3×ResNet12 (ours) 71.75 ± 0.19 87.15 ± 0.12

TieredImageNet Dataset (inductive)

Methods 1-Shot 5-Way 5-Shot 5-Way
SimpleShot [29] 69.09 ± 0.22 84.58 ± 0.16
ProtoNet [10] 65.65 ± 0.92 83.40 ± 0.65
FEAT [36] 70.80 ± 0.23 84.79 ± 0.16
PAL [8] 72.25 ± 0.72 86.95 ± 0.47
DeepEMD v2 [13] 74.29 ± 0.32 86.98 ± 0.60
MERL [38] 72.14 ± 0.51 87.01 ± 0.35
COSOC [9] 73.57 ± 0.43 87.57 ± 0.10
CNL [37] 73.42 ± 0.95 87.72 ± 0.75
invariance-equivariance [39] 72.21 ± 0.90 87.08 ± 0.58
CSEI [40] 73.76 ± 0.32 87.83 ± 0.59
ASY ResNet12 (ours) 74.31 ± 0.22 87.86 ± 0.15
above <=12M nb of parameters below 36M
S2M2R [12] 73.71 ± 0.22 88.52 ± 0.14
EASY 3×ResNet12 (ours) 74.71 ± 0.22 88.33 ± 0.14

CUBFS Dataset (inductive)

Methods 1-Shot 5-Way 5-Shot 5-Way
FEAT [36] 68.87 ± 0.22 82.90 ± 0.10
LaplacianShot [41] 80.96 88.68
ProtoNet [10] 66.09 ± 0.92 82.50 ± 0.58
DeepEMD v2 [13] 79.27 ± 0.29 89.80 ± 0.51
EASY 4×ResNet12 1/sqrt(2) 77.97 ± 0.20 91.59 ± 0.10
above <=12M nb of parameters below 36M
S2M2R [12] 80.68 ± 0.81 90.85 ± 0.44
EASY 3×ResNet12 (ours) 78.56 ± 0.19 91.93 ± 0.10

CIFAR-FS Dataset (inductive)

Methods 1-Shot 5-Way 5-Shot 5-Way
S2M2R [12] 63.66 ± 0.17 76.07 ± 0.19
R2-D2 (+ens) [20] 76.51 ± 0.47 87.63 ± 0.34
invariance-equivariance [39] 77.87 ± 0.85 89.74 ± 0.57
EASY 2×ResNet12 1/sqrt(2) (ours) 75.24 ± 0.20 88.38 ± 0.14
above <=12M nb of parameters below 36M
S2M2R [12] 74.81 ± 0.19 87.47 ± 0.13
EASY 3×ResNet12 (ours) 76.20 ± 0.20 89.00 ± 0.14

FC-100 Dataset (inductive)

Methods 1-Shot 5-Way 5-Shot 5-Way
DeepEMD v2 [13] 46.60 ± 0.26 63.22 ± 0.71
TADAM [35] 40.10 ± 0.40 56.10 ± 0.40
ProtoNet [10] 41.54 ± 0.76 57.08 ± 0.76
invariance-equivariance [39] 47.76 ± 0.77 65.30 ± 0.76
R2-D2 (+ens) [20] 44.75 ± 0.43 59.94 ± 0.41
EASY 2×ResNet12 1/sqrt(2) (ours) 47.94 ± 0.19 64.14 ± 0.19
above <=12M nb of parameters below 36M
EASY 3×ResNet12 (ours) 48.07 ± 0.19 64.74 ± 0.19

Minimagenet (transductive)

Methods 1-Shot 5-Way 5-Shot 5-Way
TIM-GD [42] 73.90 85.00
ODC [43] 77.20 ± 0.36 87.11 ± 0.42
PEMnE-BMS∗ [32] 80.56 ± 0.27 87.98 ± 0.14
SSR [44] 68.10 ± 0.60 76.90 ± 0.40
iLPC [45] 69.79 ± 0.99 79.82 ± 0.55
EPNet [31] 66.50 ± 0.89 81.60 ± 0.60
DPGN [46] 67.77 ± 0.32 84.60 ± 0.43
ECKPN [47] 70.48 ± 0.38 85.42 ± 0.46
Rot+KD+POODLE [48] 77.56 85.81
EASY 2×ResNet12( 1√2) (ours) 81.70 ±0.25 88.29 ±0.13
above <=12M nb of parameters below 36M
SSR [44] 72.40 ± 0.60 80.20 ± 0.40
fine-tuning(train+val) [49] 68.11 ± 0.69 80.36 ± 0.50
SIB+E3BM [50] 71.40 81.20
LR+DC [17] 68.57 ± 0.55 82.88 ± 0.42
EPNet [31] 70.74 ± 0.85 84.34 ± 0.53
TIM-GD [42] 77.80 87.40
PT+MAP [51] 82.92 ± 0.26 88.82 ± 0.13
iLPC [45] 83.05 ± 0.79 88.82 ± 0.42
ODC [43] 80.64 ± 0.34 89.39 ± 0.39
PEMnE-BMS∗ [32] 83.35 ± 0.25 89.53 ± 0.13
EASY 3×ResNet12 (ours) 82.75 ±0.25 88.93 ±0.12

CUB-FS (transductive)

Methods 1-Shot 5-Way 5-Shot 5-Way
TIM-GD [42] 82.20 90.80
ODC [43] 85.87 94.97
DPGN [46] 75.71 ± 0.47 91.48 ± 0.33
ECKPN [47] 77.43 ± 0.54 92.21 ± 0.41
iLPC [45] 89.00 ± 0.70 92.74 ± 0.35
Rot+KD+POODLE [48] 89.93 93.78
EASY 4×ResNet12( 1/2) (ours) 90.41 ± 0.19 93.58 ± 0.10
above <=12M nb of parameters below 36M
LR+DC [17] 79.56 ± 0.87 90.67 ± 0.35
PT+MAP [51] 91.55 ± 0.19 93.99 ± 0.10
iLPC [45] 91.03 ± 0.63 94.11 ± 0.30
EASY 3×ResNet12 (ours) 90.76 ± 0.19 93.90 ± 0.09

CIFAR-FS (transductive)

Methods 1-Shot 5-Way 5-Shot 5-Way
SSR [44] 76.80 ± 0.60 83.70 ± 0.40
iLPC [45] 77.14 ± 0.95 85.23 ± 0.55
DPGN [46] 77.90 ± 0.50 90.02 ± 0.40
ECKPN [47] 79.20 ± 0.40 91.00 ± 0.50
EASY 2×ResNet12 (1/sqrt(2)) (ours) 86.40 ± 0.23 89.75 ± 0.15
above <=12M nb of parameters below 36M
SSR [44] 81.60 ± 0.60 86.00 ± 0.40
fine-tuning (train+val) [49] 78.36 ± 0.70 87.54 ± 0.49
iLPC [45] 86.51 ± 0.75 90.60 ± 0.48
PT+MAP [51] 87.69 ± 0.23 90.68 ± 0.15
EASY 3×ResNet12 (ours) 86.96 ± 0.22 90.30 ± 0.15

FC-100 (transductive)

Methods 1-Shot 5-Way 5-Shot 5-Way
EASY 2×ResNet12( 1√2)(ours) 54.68 ± 0.25 66.19 ± 0.20
above <=12M nb of parameters below 36M
SIB+E3BM [50] 46.00 57.10
fine-tuning (train) [49] 43.16 ± 0.59 57.57 ± 0.55
ODC [43] 47.18 ± 0.30 59.21 ± 0.56
fine-tuning (train+val) [49] 50.44 ± 0.68 65.74 ± 0.60
EASY 3×ResNet12 (ours) 55.11 ± 0.25 67.09 ± 0.20

Tiered Imagenet (transducive)

Methods 1-Shot 5-Way 5-Shot 5-Way
PT+MAP [51] 85.67 ± 0.26 90.45 ± 0.14
TIM-GD [42] 79.90 88.50
ODC [43] 83.73 ± 0.36 90.46 ± 0.46
SSR [44] 81.20 ± 0.60 85.70 ± 0.40
Rot+KD+POODLE [48] 79.67 86.96
DPGN [46] 72.45 ± 0.51 87.24 ± 0.39
EPNet [31] 76.53 ± 0.87 87.32 ± 0.64
ECKPN [47] 73.59 ± 0.45 88.13 ± 0.28
iLPC [45] 83.49 ± 0.88 89.48 ± 0.47
ASY ResNet12 (ours) 82.66 ± 0.27 88.60 ± 0.14
above <=12M nb of parameters below 36M
SIB+E3BM [50] 75.60 84.30
SSR [44] 79.50 ± 0.60 84.80 ± 0.40
fine-tuning (train+val) [49] 72.87 ± 0.71 86.15 ± 0.50
TIM-GD [42] 82.10 89.80
LR+DC [17] 78.19 ± 0.25 89.90 ± 0.41
EPNet [31] 78.50 ± 0.91 88.36 ± 0.57
ODC [43] 85.22 ± 0.34 91.35 ± 0.42
iLPC [45] 88.50 ± 0.75 92.46 ± 0.42
PEMnE-BMS∗ [32] 86.07 ± 0.25 91.09 ± 0.14
EASY 3×ResNet12 (ours) 84.48 ± 0.27 89.71 ± 0.14
Owner
Yassir BENDOU
Ph.D student working on Few-shot learning problems. I enjoy maths and coding.
Yassir BENDOU
GitHub Repository
Predict stock movement with Machine Learning and Deep Learning algorithms

Project Overview Stock market movement prediction using LSTM Deep Neural Networks and machine learning algorithms Software and Library Requirements Th

Naz Delam 46 Sep 13, 2022
Fight Recognition from Still Images in the Wild @ WACVW2022, Real-world Surveillance Workshop

Fight Detection from Still Images in the Wild Detecting fights from still images is an important task required to limit the distribution of social med

Şeymanur Aktı 10 Nov 09, 2022
v objective diffusion inference code for JAX.

v-diffusion-jax v objective diffusion inference code for JAX, by Katherine Crowson (@RiversHaveWings) and Chainbreakers AI (@jd_pressman). The models

Katherine Crowson 186 Dec 21, 2022
PyTorch code for our ECCV 2020 paper "Single Image Super-Resolution via a Holistic Attention Network"

HAN PyTorch code for our ECCV 2020 paper "Single Image Super-Resolution via a Holistic Attention Network" This repository is for HAN introduced in the

五维空间 140 Nov 23, 2022
Self-supervised Deep LiDAR Odometry for Robotic Applications

DeLORA: Self-supervised Deep LiDAR Odometry for Robotic Applications Overview Paper: link Video: link ICRA Presentation: link This is the correspondin

Robotic Systems Lab - Legged Robotics at ETH Zürich 181 Dec 29, 2022
The Environment I built to study Reinforcement Learning + Pokemon Showdown

pokemon-showdown-rl-environment The Environment I built to study Reinforcement Learning + Pokemon Showdown Been a while since I ran this. Think it is

3 Jan 16, 2022
3rd Place Solution of the Traffic4Cast Core Challenge @ NeurIPS 2021

3rd Place Solution of Traffic4Cast 2021 Core Challenge This is the code for our solution to the NeurIPS 2021 Traffic4Cast Core Challenge. Paper Our so

7 Jul 25, 2022
Mini-hmc-jax - A simple implementation of Hamiltonian Monte Carlo in JAX

mini-hmc-jax This is a simple implementation of Hamiltonian Monte Carlo in JAX t

Martin Marek 6 Mar 03, 2022
TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction

TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction TSDF++ is a novel multi-object TSDF formulation that can encode mult

ETHZ ASL 130 Dec 29, 2022
The official implementation of Equalization Loss for Long-Tailed Object Recognition (CVPR 2020) based on Detectron2

Equalization Loss for Long-Tailed Object Recognition Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, Junjie Yan ⚠️ We re

Jingru Tan 197 Dec 25, 2022
A Deep Learning Framework for Neural Derivative Hedging

NNHedge NNHedge is a PyTorch based framework for Neural Derivative Hedging. The following repository was implemented to ease the experiments of our pa

GUIJIN SON 17 Nov 14, 2022
A modular active learning framework for Python

Modular Active Learning framework for Python3 Page contents Introduction Active learning from bird's-eye view modAL in action From zero to one in a fe

modAL 1.9k Dec 31, 2022
Voxel Transformer for 3D object detection

Voxel Transformer This is a reproduced repo of Voxel Transformer for 3D object detection. The code is mainly based on OpenPCDet. Introduction We provi

173 Dec 25, 2022
Video-Music Transformer

VMT Video-Music Transformer (VMT) is an attention-based multi-modal model, which generates piano music for a given video. Paper https://arxiv.org/abs/

Chin-Tung Lin 5 Jul 13, 2022
An implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I

Simon Niklaus 984 Dec 16, 2022
Ontologysim: a Owlready2 library for applied production simulation

Ontologysim: a Owlready2 library for applied production simulation Ontologysim is an open-source deep production simulation framework, with an emphasi

10 Nov 30, 2022
Pytorch implementation of Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization https://arxiv.org/abs/2008.11646

[TCSVT] Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization LPN [Paper] NEWs Prerequisites Python 3.6 GPU Memory = 8G Numpy 1.

46 Dec 14, 2022
CC-GENERATOR - A python script for generating CC

CC-GENERATOR A python script for generating CC NOTE: This tool is for Educationa

Lêkzï 6 Oct 14, 2022
Implementation of UNET architecture for Image Segmentation.

Semantic Segmentation using UNET This is the implementation of UNET on Carvana Image Masking Kaggle Challenge About the Dataset This dataset contains

Anushka agarwal 4 Dec 21, 2021
This repository implements variational graph auto encoder by Thomas Kipf.

Variational Graph Auto-encoder in Pytorch This repository implements variational graph auto-encoder by Thomas Kipf. For details of the model, refer to

DaehanKim 215 Jan 02, 2023