CIFAR-10 (Canadian Institute for Advanced Research, 10 classes)

Introduced by Krizhevsky et al. in Learning multiple layers of features from tiny images

The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.

Task	Dataset Variant	Best Model
Image Classification	CIFAR-10	ViT-H/14
Image Generation	CIFAR-10	StyleSAN-XL
Long-tail Learning	CIFAR-10-LT (ρ=10)	GLMC+MaxNorm
Semi-Supervised Image Classification	CIFAR-10, 4000 Labels	SemCo
Neural Architecture Search	CIFAR-10	NAT-M4
Anomaly Detection	One-class CIFAR-10	CLIP
Image Clustering	CIFAR-10	TEMI CLIP ViT-L
Long-tail Learning	CIFAR-10-LT (ρ=100)	GLMC+MaxNorm
Conditional Image Generation	CIFAR-10	EDM-G++
Semi-Supervised Image Classification	CIFAR-10, 250 Labels	Dash
Neural Architecture Search	CIFAR-10 Image Classification	NAT-M4
Semi-Supervised Image Classification	CIFAR-10, 40 Labels	FreeMatch
Graph Classification	CIFAR10 100k	GRIT
Out-of-Distribution Detection	CIFAR-10 vs CIFAR-100	DHM
Density Estimation	CIFAR-10	MuLAN
Out-of-Distribution Detection	CIFAR-10	DHM
Image Classification	cifar10	SAM
Semi-Supervised Image Classification	CIFAR-10, 1000 Labels	MixMatch
Adversarial Defense	CIFAR-10	WRN-28-10
Sequential Image Classification	noise padded CIFAR-10	LEM
Unsupervised Image Classification	CIFAR-10	SeCu
Personalized Federated Learning	CIFAR-10	pFedHN-PC
Active Learning	CIFAR10 (10,000)	TypiClust
Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly	cifar10	Shell-Renormalized
Sparse Learning and binarization	CIFAR-10	SBNN-50%
Anomaly Detection	Leave-One-Class-Out CIFAR-10	BCE-CLIP
Unsupervised Anomaly Detection with Specified Settings -- 30% anomaly	CIFAR-10	Shell-Renormalized
Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly	CIFAR-10	LVAD
Unsupervised Anomaly Detection with Specified Settings -- 1% anomaly	CIFAR-10	LVAD
Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly	CIFAR-10	LVAD
Small Data Image Classification	CIFAR-10, 500 Labels	ChimeraMix+AutoAugment
Stochastic Optimization	CIFAR-10 WRN-28-10 - 200 Epochs	Adam
Classification with Binary Weight Network	CIFAR-10	WRN-28-10
Neural Network Compression	CIFAR-10	ShuffleNet – Quantised
Data Augmentation	CIFAR-10	Shake-Shake
Small Data Image Classification	CIFAR-10, 1000 Labels	ChimeraMix+AutoAugment
Small Data Image Classification	CIFAR-10, 100 Labels	ChimeraMix+AutoAugment
Open-World Semi-Supervised Learning	CIFAR-10	OpenLDN
Stochastic Optimization	CIFAR-10 ResNet-18 - 200 Epochs	SGD - cosine LR schedule
Semi-Supervised Image Classification	cifar10, 250 Labels	ReMixMatch
Adversarial Attack	CIFAR-10	Xu et al.
Adversarial Robustness	CIFAR-10	Mixed classifier
Provable Adversarial Defense	CIFAR-10	SLL X-Large
Image Classification	CIFAR-10 (with noisy labels)	PGDF
Semi-Supervised Image Classification	CIFAR-10, 2000 Labels	MixMatch
Network Pruning	CIFAR-10	TAS-pruned ResNet-110
Semi-Supervised Image Classification	CIFAR-10, 20 Labels	MutexMatch
Supervised Image Retrieval	CIFAR-10	SSB-VAE
Image Generation	CIFAR-10 (20% data)	DiffAugment-CR-BigGAN
Image Generation	CIFAR-10 (10% data)	DiffAugment-StyleGAN2
Semi-Supervised Image Classification	cifar-10, 10 Labels	BOSS
Classification with Binary Neural Network	CIFAR-10	AdaBin
Image Classification with Label Noise	CIFAR-10, 20% IDN	CORES2*
Image Classification with Label Noise	CIFAR-10, 40% IDN	CORES2*
Image Classification with Label Noise	CIFAR-10, 60% IDN	CORES2*
Clean-label Backdoor Attack (0.05%)	CIFAR-10	GoogLeNet
Robust classification	CIFAR-10	ResNet-110 w/ Adv. Training
Image Classification	CIFAR-10 Image Classification	ASF-former-S
Small Data Image Classification	cifar10, 10 labels	VAE
Semi-Supervised Image Classification (Cold Start)	CIFAR-10, 100 Labels	SimCLR-kmediods-PAWS
Binarization	CIFAR-10	WRN-28-10
Self-Supervised Learning	cifar10	ResNet50
Neural Architecture Search	NATS-Bench Size, CIFAR-10	BossNAS
Stochastic Optimization	CIFAR-10	Resnet18
Quantization	CIFAR-10	3DCNN_VIVA_3
Nature-Inspired Optimization Algorithm	CIFAR-10	Position-wise optimizer
Semi-Supervised Image Classification	CIFAR-10, 80 Labels	MutexMatch
Image Classification	CIFAR-10, 40% Symmetric Noise	FaMUS
Image Classification	CIFAR-10, 60% Symmetric Noise	MentorMix
Self-Supervised Learning	CIFAR-10	CorInfomax
Semi-Supervised Image Classification	CIFAR-10, 500 Labels	MixMatch
Density Estimation	CIFAR-10 (Conditional)	MAF
Hard-label Attack	CIFAR-10	RayS
Small Data Image Classification	CIFAR-10, 250 Labels	GLICO
Anomaly Detection	CIFAR-10	RCALAD
Image Classification	cifar-10,4000	WRN-28-2 + UDA+AutoDropout
Image Classification with Human Noise	CIFAR-10, Human Noise	HOC
Image Classification with Label Noise	CIFAR-10, 40% Symmetric Noise	CORES2*
Image Classification with Label Noise	CIFAR-10, 60% Symmetric Noise	CORES2*
Image Classification with Label Noise	CIFAR-10, 20% Asymmetric Noise	CORES2*
Image Classification with Label Noise	CIFAR-10, 30% Asymmetric Noise	CORES2*
Image Compression	CIFAR-10	Lossyless Compressor
Domain-IL Continual Learning	Cifar10 (5 tasks)	HVCL
Image Classification	CIFAR10_CATS	CatsNet
Model Poisoning	CIFAR-10	ADA
Image Retrieval	CIFAR-10	Custom: 3 conv + 2 fcn
Image Classification with Label Noise	CIFAR-10	PropMix
Image Classification	cifar10_quality_drift	resnet-50-cifar10-quality-drift
Novel Class Discovery	cifar10	AutoNovel
Learning with noisy labels	CIFAR-10	ILL
Out-of-Distribution Detection	cifar10	Wideresnet 40
Long-tail Learning on CIFAR-10-LT (ρ=100)	CIFAR-10-LT (ρ=100)	BCL
Semi-Supervised Image Classification	CIFAR-10, 100 Labels	SimCLR-kmediods-PAWS
Semi-Supervised Image Classification	CIFAR-10, 30 Labels	SimCLR-kmediods-PAWS
Semi-Supervised Image Classification (Cold Start)	CIFAR-10, 30 Labels	SimCLR-kmediods-PAWS
Semi-Supervised Image Classification (Cold Start)	CIFAR-10, 40 Labels	FixMatch-USL-T
Partial Label Learning	CIFAR-10 (partial ratio 0.1)	ILL
Partial Label Learning	CIFAR-10 (partial ratio 0.3)	ILL
Partial Label Learning	CIFAR-10 (partial ratio 0.5)	ILL
Continual Learning	Split CIFAR-10 (5 tasks)	H$^{2}$

CIFAR-10 (Canadian Institute for Advanced Research, 10 classes)

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