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

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

Related tags

Overview

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

Downloads

Testing scripts for EASY

Inductive setup using NCM

Transductive setup using Soft k-means

Training scripts for Y

Important Arguments

Few-shot classification Results

Owner

Yassir BENDOU

Telegram chatbot created with deep learning model (LSTM) and telebot library.

Code repo for "RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network" (Machine Learning and the Physical Sciences workshop in NeurIPS 2021).

This is the source code of the solver used to compete in the International Timetabling Competition 2019.

Code release for Universal Domain Adaptation(CVPR 2019)

Codes for TIM2021 paper "Anchor-Based Spatio-Temporal Attention 3-D Convolutional Networks for Dynamic 3-D Point Cloud Sequences"

Research code for the paper "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models"

Spectralformer: Rethinking hyperspectral image classification with transformers

City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones Code

学习 python3 以来写的一些垃圾玩具……

High-performance moving least squares material point method (MLS-MPM) solver.

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

AutoML library for deep learning

Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation

DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

As-ViT: Auto-scaling Vision Transformers without Training

Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations

A minimal yet resourceful implementation of diffusion models (along with pretrained models + synthetic images for nine datasets)

[IROS'21] SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning

WiFi-based Multi-task Sensing

face2comics by Sxela (Alex Spirin) - face2comics datasets