Image Classification on SVHN

Metric: Percentage error (lower is better)

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#	Model↕	Percentage error▲	Extra Data	Paper	Date↕	Code
1	E2E-M3	1	No	Rethinking Recurrent Neural Networks and Other I...	2020-07-30	Code
2	Wide-ResNet-28-10 (Fast AA)	1.1	No	Fast AutoAugment	2019-05-01	Code
3	Colornet	1.11	No	ColorNet: Investigating the importance of color ...	2019-02-01	Code
4	PBA [ho2019pba]	1.2	No	Population Based Augmentation: Efficient Learnin...	2019-05-14	Code
5	WaveMixLite-144/15	1.27	No	WaveMix: A Resource-efficient Neural Network for...	2022-05-28	Code
6	Cutout	1.3	No	Improved Regularization of Convolutional Neural ...	2017-08-15	Code
7	PyramidNet + AA (AMP)	1.35	No	Regularizing Neural Networks via Adversarial Mod...	2020-10-10	Code
8	WRN + fixup init + mixup + cutout	1.4	No	Fixup Initialization: Residual Learning Without ...	2019-01-27	Code
9	Drop-Activation	1.46	No	Drop-Activation: Implicit Parameter Reduction an...	2018-11-14	Code
10	SOPCNN	1.5	No	Stochastic Optimization of Plain Convolutional N...	2020-01-24	Code
11	EraseReLU	1.54	No	EraseReLU: A Simple Way to Ease the Training of ...	2017-09-22	-
12	Wide Residual Networks	1.54	No	Wide Residual Networks	2016-05-23	Code
13	CoPaNet-R-164	1.58	No	Deep Competitive Pathway Networks	2017-09-29	Code
14	WaveMixLite	1.58	No	-	-	Code
15	DenseNet	1.59	No	Densely Connected Convolutional Networks	2016-08-25	Code
16	Multilevel Residual Networks	1.59	No	Residual Networks of Residual Networks: Multilev...	2016-08-09	Code
17	VGG8B + LocalLearning + CO	1.65	No	Training Neural Networks with Local Error Signals	2019-01-20	Code
18	Tree+Max-Avg pooling	1.7	No	Generalizing Pooling Functions in Convolutional ...	2015-09-30	Code
19	Wide ResNet	1.7	No	Wide Residual Networks	2016-05-23	Code
20	Stochastic Depth	1.75	No	Deep Networks with Stochastic Depth	2016-03-30	Code
21	RCNN-96	1.8	No	-	-	-
22	BNM NiN	1.8	No	Batch-normalized Maxout Network in Network	2015-11-09	Code
23	CMsC	1.8	No	Competitive Multi-scale Convolution	2015-11-18	-
24	Regularization of Neural Networks using DropConnect	1.9	No	-	-	-
25	DSN	1.9	No	Deeply-Supervised Nets	2014-09-18	Code
26	MLR DNN	1.9	No	-	-	-
27	MIM	2	No	On the Importance of Normalisation Layers in Dee...	2015-08-03	-
28	FractalNet	2.01	No	FractalNet: Ultra-Deep Neural Networks without R...	2016-05-24	Code
29	DCNN	2.2	No	Multi-digit Number Recognition from Street View ...	2013-12-20	Code
30	BinaryConnect	2.2	No	BinaryConnect: Training Deep Neural Networks wit...	2015-11-02	Code
31	EXACT (WRN-16-8)	2.21	No	EXACT: How to Train Your Accuracy	2022-05-19	Code
32	EnAET	2.22	No	EnAET: A Self-Trained framework for Semi-Supervi...	2019-11-21	Code
33	PreActResNet18 (AMP)	2.3	No	Regularizing Neural Networks via Adversarial Mod...	2020-10-10	Code
34	Network in Network	2.35	No	Network In Network	2013-12-16	Code
35	ReNet	2.4	No	ReNet: A Recurrent Neural Network Based Alternat...	2015-05-03	Code
36	Maxout	2.5	No	Maxout Networks	2013-02-18	Code
37	MixMatch	2.59	No	MixMatch: A Holistic Approach to Semi-Supervised...	2019-05-06	Code
38	ResNet-18	2.65	No	Benchopt: Reproducible, efficient and collaborat...	2022-06-27	Code
39	Stochastic Pooling	2.8	No	Stochastic Pooling for Regularization of Deep Co...	2013-01-16	Code
40	Deep Complex	3.3	No	Deep Complex Networks	2017-05-27	Code
41	FLSCNN	4	No	Enhanced Image Classification With a Fast-Learni...	2015-03-16	-
42	Convolutional neural networks applied to house numbers digit classification	4.9	No	-	-	-
43	CLS-GAN	5.98	No	Loss-Sensitive Generative Adversarial Networks o...	2017-01-23	Code
44	Improved GAN	8.11	No	Improved Techniques for Training GANs	2016-06-10	Code
45	Local Mixup LeNet	8.2	No	Preventing Manifold Intrusion with Locality: Loc...	2022-01-12	Code
46	Sign-symmetry	10.16	No	How Important is Weight Symmetry in Backpropagat...	2015-10-17	Code
47	SEER (RegNet10B)	13.6	Yes	Vision Models Are More Robust And Fair When Pret...	2022-02-16	Code
48	ANODE	16.5	No	Augmented Neural ODEs	2019-04-02	Code
49	Skip DGN	16.61	No	Auxiliary Deep Generative Models	2016-02-17	Code
50	DCGAN	22.48	No	Unsupervised Representation Learning with Deep C...	2015-11-19	Code
51	Auxiliary DGN	22.86	No	Auxiliary Deep Generative Models	2016-02-17	Code
52	Supervised CNN	28.87	No	Unsupervised Representation Learning with Deep C...	2015-11-19	Code
53	M1+M2	36.02	No	Semi-Supervised Learning with Deep Generative Mo...	2014-06-20	Code
54	DGN	36.02	No	Semi-Supervised Learning with Deep Generative Mo...	2014-06-20	Code
55	M1+TSVM	54.33	No	Semi-Supervised Learning with Deep Generative Mo...	2014-06-20	Code
56	M1+KNN	65.63	No	Semi-Supervised Learning with Deep Generative Mo...	2014-06-20	Code
57	TSVM	66.55	No	Unsupervised Representation Learning with Deep C...	2015-11-19	Code
58	KNN	77.93	No	Unsupervised Representation Learning with Deep C...	2015-11-19	Code

#1E2E-M3SOTA
1
Percentage error· 2020-07-30
Rethinking Recurrent Neural Networks and Other Improvements for Image Classification Code
#2Wide-ResNet-28-10 (Fast AA)SOTA
1.1
Percentage error· 2019-05-01
Fast AutoAugment Code
#3ColornetSOTA
1.11
Percentage error· 2019-02-01
ColorNet: Investigating the importance of color spaces for image classification Code
#4PBA [ho2019pba]
1.2
Percentage error· 2019-05-14
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules Code
#5WaveMixLite-144/15
1.27
Percentage error· 2022-05-28
WaveMix: A Resource-efficient Neural Network for Image Analysis Code
#6CutoutSOTA
1.3
Percentage error· 2017-08-15
Improved Regularization of Convolutional Neural Networks with Cutout Code
#7PyramidNet + AA (AMP)
1.35
Percentage error· 2020-10-10
Regularizing Neural Networks via Adversarial Model Perturbation Code
#8WRN + fixup init + mixup + cutout
1.4
Percentage error· 2019-01-27
Fixup Initialization: Residual Learning Without Normalization Code
#9Drop-Activation
1.46
Percentage error· 2018-11-14
Drop-Activation: Implicit Parameter Reduction and Harmonic Regularization Code
#10SOPCNN
1.5
Percentage error· 2020-01-24
Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods Code
#11EraseReLU
1.54
Percentage error· 2017-09-22
EraseReLU: A Simple Way to Ease the Training of Deep Convolution Neural Networks
#12Wide Residual NetworksSOTA
1.54
Percentage error· 2016-05-23
Wide Residual Networks Code
#13CoPaNet-R-164
1.58
Percentage error· 2017-09-29
Deep Competitive Pathway Networks Code
#14WaveMixLite
1.58
Percentage error
No paperCode
#15DenseNet
1.59
Percentage error· 2016-08-25
Densely Connected Convolutional Networks Code
#16Multilevel Residual Networks
1.59
Percentage error· 2016-08-09
Residual Networks of Residual Networks: Multilevel Residual Networks Code
#17VGG8B + LocalLearning + CO
1.65
Percentage error· 2019-01-20
Training Neural Networks with Local Error Signals Code
#18Tree+Max-Avg poolingSOTA
1.7
Percentage error· 2015-09-30
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree Code
#19Wide ResNet
1.7
Percentage error· 2016-05-23
Wide Residual Networks Code
#20Stochastic Depth
1.75
Percentage error· 2016-03-30
Deep Networks with Stochastic Depth Code
#21RCNN-96
1.8
Percentage error
No paper
#22BNM NiN
1.8
Percentage error· 2015-11-09
Batch-normalized Maxout Network in Network Code
#23CMsC
1.8
Percentage error· 2015-11-18
Competitive Multi-scale Convolution
#24Regularization of Neural Networks using DropConnect
1.9
Percentage error
No paper
#25DSNSOTA
1.9
Percentage error· 2014-09-18
Deeply-Supervised Nets Code
#26MLR DNN
1.9
Percentage error
No paper
#27MIM
2
Percentage error· 2015-08-03
On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units
#28FractalNet
2.01
Percentage error· 2016-05-24
FractalNet: Ultra-Deep Neural Networks without Residuals Code
#29DCNNSOTA
2.2
Percentage error· 2013-12-20
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks Code
#30BinaryConnect
2.2
Percentage error· 2015-11-02
BinaryConnect: Training Deep Neural Networks with binary weights during propagations Code
#31EXACT (WRN-16-8)
2.21
Percentage error· 2022-05-19
EXACT: How to Train Your Accuracy Code
#32EnAET
2.22
Percentage error· 2019-11-21
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations Code
#33PreActResNet18 (AMP)
2.3
Percentage error· 2020-10-10
Regularizing Neural Networks via Adversarial Model Perturbation Code
#34Network in NetworkSOTA
2.35
Percentage error· 2013-12-16
Network In Network Code
#35ReNet
2.4
Percentage error· 2015-05-03
ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks Code
#36MaxoutSOTA
2.5
Percentage error· 2013-02-18
Maxout Networks Code
#37MixMatch
2.59
Percentage error· 2019-05-06
MixMatch: A Holistic Approach to Semi-Supervised Learning Code
#38ResNet-18
2.65
Percentage error· 2022-06-27
Benchopt: Reproducible, efficient and collaborative optimization benchmarks Code
#39Stochastic PoolingSOTA
2.8
Percentage error· 2013-01-16
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks Code
#40Deep Complex
3.3
Percentage error· 2017-05-27
Deep Complex Networks Code
#41FLSCNN
4
Percentage error· 2015-03-16
Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network
#42Convolutional neural networks applied to house numbers digit classification
4.9
Percentage error
No paper
#43CLS-GAN
5.98
Percentage error· 2017-01-23
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities Code
#44Improved GAN
8.11
Percentage error· 2016-06-10
Improved Techniques for Training GANs Code
#45Local Mixup LeNet
8.2
Percentage error· 2022-01-12
Preventing Manifold Intrusion with Locality: Local Mixup Code
#46Sign-symmetry
10.16
Percentage error· 2015-10-17
How Important is Weight Symmetry in Backpropagation?Code
#47SEER (RegNet10B)
13.6
Percentage error· Extra Data· 2022-02-16
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision Code
#48ANODE
16.5
Percentage error· 2019-04-02
Augmented Neural ODEs Code
#49Skip DGN
16.61
Percentage error· 2016-02-17
Auxiliary Deep Generative Models Code
#50DCGAN
22.48
Percentage error· 2015-11-19
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Code
#51Auxiliary DGN
22.86
Percentage error· 2016-02-17
Auxiliary Deep Generative Models Code
#52Supervised CNN
28.87
Percentage error· 2015-11-19
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Code
#53M1+M2
36.02
Percentage error· 2014-06-20
Semi-Supervised Learning with Deep Generative Models Code
#54DGN
36.02
Percentage error· 2014-06-20
Semi-Supervised Learning with Deep Generative Models Code
#55M1+TSVM
54.33
Percentage error· 2014-06-20
Semi-Supervised Learning with Deep Generative Models Code
#56M1+KNN
65.63
Percentage error· 2014-06-20
Semi-Supervised Learning with Deep Generative Models Code
#57TSVM
66.55
Percentage error· 2015-11-19
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Code
#58KNN
77.93
Percentage error· 2015-11-19
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Code