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

Model search is a framework that implements AutoML algorithms for model architecture search at scale

Hl classification bc - A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality

PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

The 1st Place Solution of the Facebook AI Image Similarity Challenge (ISC21) : Descriptor Track.

A curated list of awesome resources related to Semantic Search🔎 and Semantic Similarity tasks.

A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

Use unsupervised and supervised learning to predict stocks

PyTorch implementation of MSBG hearing loss model and MBSTOI intelligibility metric

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

An introduction to bioimage analysis - http://bioimagebook.github.io

Code for CVPR 2021 oral paper "Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts"

The codebase for Data-driven general-purpose voice activity detection.

ZSL-KG is a general-purpose zero-shot learning framework with a novel transformer graph convolutional network (TrGCN) to learn class representation from common sense knowledge graphs.

JAX bindings to the Flatiron Institute Non-uniform Fast Fourier Transform (FINUFFT) library

Pytorch Implementation for NeurIPS (oral) paper: Pixel Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation

In the AI for TSP competition we try to solve optimization problems using machine learning.

Official codebase for Pretrained Transformers as Universal Computation Engines.

[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

《Image2Reverb: Cross-Modal Reverb Impulse Response Synthesis》(2021)