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Datasets/CIFAR-10

CIFAR-10

ImagesIntroduced 2022-10-14

Benchmarks

2D Classification/Size (MB)Adversarial Attack/Attack: PGD20 Adversarial Attack/Attack: AutoAttack Adversarial Attack/Attack: DeepFool Adversarial Attack/Robust Accuracy Adversarial Defense/Accuracy Adversarial Defense/Attack: AutoAttack Adversarial Defense/Robust Accuracy Adversarial Robustness/Accuracy Adversarial Robustness/Robust Accuracy Adversarial Robustness/Attack: AutoAttack Anomaly Detection/Mean AUC Anomaly Detection/AUC-ROC AutoML/Top-1 Error Rate AutoML/Parameters AutoML/FLOPS AutoML/Search Time (GPU days)AutoML/Accuracy (% )Classification/Accuracy Conditional Image Generation/FID Conditional Image Generation/Inception score Conditional Image Generation/Intra-FID Contrastive Learning/Accuracy (Top-1)Data Augmentation/Percentage error Density Estimation/NLL (bits/dim)Density Estimation/Log-likelihood (nats)Document Text Classification/Test Accuracy Federated Learning/ACC@1-10Clients Federated Learning/ACC@1-50Clients Federated Learning/ACC@1-100Clients Federated Learning/ACC@1-500Clients Graph Classification/Accuracy Image Classification/Percentage correct Image Classification/Top-1 Accuracy Image Classification/Accuracy Image Classification/Parameters Image Classification/Top 1 Accuracy Image Classification/F1 Image Classification/Cross Entropy Loss Image Classification/AUCROC Image Classification/Test Accuracy Image Classification/All accuracy (10% Labeled)Image Classification/All accuracy (50% Labeled)Image Classification/Novel accuracy (10% Labeled)Image Classification/Novel accuracy (50% Labeled)Image Classification/Seen accuracy (10% Labeled)Image Classification/Seen accuracy (50% Labeled)Image Classification/Accuracy Image Clustering/Accuracy Image Clustering/NMI Image Clustering/ARI Image Clustering/Train set Image Clustering/Backbone Image Compression/Bit rate Image Generation/FID Image Generation/IS Image Generation/NFE Image Generation/Inception score Image Generation/Intra-FID Image Retrieval/Average-mAP Model Compression/Size (MB)Nature-Inspired Optimization Algorithm/training time (s)Network Pruning/Accuracy Network Pruning/GFLOPs Network Pruning/Inference Time (ms)Neural Architecture Search/Top-1 Error Rate Neural Architecture Search/Parameters Neural Architecture Search/FLOPS Neural Architecture Search/Search Time (GPU days)Neural Architecture Search/Accuracy (% )Out-of-Distribution Detection/AUROC Out-of-Distribution Detection/FPR95 Quantization/MAP Self-Supervised Learning/Top-1 Accuracy Semi-Supervised Image Classification/All accuracy (10% Labeled)Semi-Supervised Image Classification/All accuracy (50% Labeled)Semi-Supervised Image Classification/Novel accuracy (10% Labeled)Semi-Supervised Image Classification/Novel accuracy (50% Labeled)Semi-Supervised Image Classification/Seen accuracy (10% Labeled)Semi-Supervised Image Classification/Seen accuracy (50% Labeled)Stochastic Optimization/Accuracy (max)Stochastic Optimization/Accuracy (mean)Supervised Image Retrieval/Precision@100 Unsupervised Anomaly Detection/AUC-ROC Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly/AUC-ROC Unsupervised Anomaly Detection with Specified Settings -- 1% anomaly/AUC-ROC Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly/AUC-ROC Unsupervised Anomaly Detection with Specified Settings -- 30% anomaly/AUC-ROC Zero-Shot Learning/Accuracy

Related Benchmarks

CIFAR-10 (10% data)/Image Generation/FID CIFAR-10 (20% data)/Image Generation/FID CIFAR-10 (250 Labels, ImageNet-100 Unlabeled)/Image Classification/Accuracy CIFAR-10 (250 Labels, ImageNet-100 Unlabeled)/Semi-Supervised Image Classification/Accuracy CIFAR-10 (40 Labels, ImageNet-100 Unlabeled)/Image Classification/Accuarcy CIFAR-10 (4000 Labels, ImageNet-100 Unlabeled)/Image Classification/Accuracy CIFAR-10 (4000 Labels, ImageNet-100 Unlabeled)/Semi-Supervised Image Classification/Accuracy CIFAR-10 (Conditional)/Density Estimation/Log-likelihood CIFAR-10 (partial ratio 0.1)/Partial Label Learning/Accuracy CIFAR-10 (partial ratio 0.3)/Partial Label Learning/Accuracy CIFAR-10 (partial ratio 0.5)/Partial Label Learning/Accuracy CIFAR-10 (with noisy labels)/Image Classification/Accuracy (under 20% Sym. label noise)CIFAR-10 (with noisy labels)/Image Classification/Accuracy (under 50% Sym. label noise)CIFAR-10 (with noisy labels)/Image Classification/Accuracy (under 80% Sym. label noise)CIFAR-10 (with noisy labels)/Image Classification/Accuracy (under 90% Sym. label noise)CIFAR-10 (with noisy labels)/Image Classification/Accuracy (under 95% Sym. label noise)CIFAR-10 Image Classification/AutoML/FLOPS CIFAR-10 Image Classification/AutoML/Params CIFAR-10 Image Classification/AutoML/Percentage error CIFAR-10 Image Classification/AutoML/Search Time (GPU days)CIFAR-10 Image Classification/Image Classification/Params CIFAR-10 Image Classification/Neural Architecture Search/FLOPS CIFAR-10 Image Classification/Neural Architecture Search/Params CIFAR-10 Image Classification/Neural Architecture Search/Percentage error CIFAR-10 Image Classification/Neural Architecture Search/Search Time (GPU days)CIFAR-10 LT/Conditional Image Generation/FID CIFAR-10 LT/Image Generation/FID CIFAR-10 ResNet-18 - 200 Epochs/Stochastic Optimization/Accuracy CIFAR-10 WRN-28-10 - 200 Epochs/Stochastic Optimization/Accuracy CIFAR-10 vs CIFAR-10.1/Out-of-Distribution Detection/AUROC CIFAR-10 vs CIFAR-10.1 (1000 samples)/Two-sample testing/Avg accuracy CIFAR-10 vs CIFAR-100/Out-of-Distribution Detection/AUPR CIFAR-10 vs CIFAR-100/Out-of-Distribution Detection/AUROC CIFAR-10 vs CIFAR-100/Out-of-Distribution Detection/FPR95 CIFAR-10 vs Gaussian/Out-of-Distribution Detection/AUROC CIFAR-10 vs ImageNet (C)/Out-of-Distribution Detection/AUROC CIFAR-10 vs ImageNet (R)/Out-of-Distribution Detection/AUROC CIFAR-10 vs LSUN (C)/Out-of-Distribution Detection/AUROC CIFAR-10 vs LSUN (R)/Out-of-Distribution Detection/AUROC CIFAR-10 vs SVHN/Out-of-Distribution Detection/AUROC CIFAR-10 vs SVHN/Out-of-Distribution Detection/FPR95 CIFAR-10 vs Uniform/Out-of-Distribution Detection/AUROC CIFAR-10 vs iSUN/Out-of-Distribution Detection/AUROC CIFAR-10, 100 Labels/Image Classification/Accuracy (%)CIFAR-10, 100 Labels/Image Classification/Percentage error CIFAR-10, 100 Labels/Semi-Supervised Image Classification/Percentage error CIFAR-10, 100 Labels (OpenSet, 6/4)/Image Classification/Accuracy CIFAR-10, 100 Labels (OpenSet, 6/4)/Semi-Supervised Image Classification/Accuracy CIFAR-10, 1000 Labels/Image Classification/Accuracy CIFAR-10, 1000 Labels/Image Classification/Accuracy (%)CIFAR-10, 1000 Labels/Semi-Supervised Image Classification/Accuracy CIFAR-10, 20 Labels/Image Classification/Percentage error CIFAR-10, 20 Labels/Semi-Supervised Image Classification/Percentage error CIFAR-10, 2000 Labels/Image Classification/Accuracy CIFAR-10, 2000 Labels/Semi-Supervised Image Classification/Accuracy CIFAR-10, 250 Labels/Image Classification/Percentage error CIFAR-10, 250 Labels/Image Classification/Top-1 accuracy %CIFAR-10, 250 Labels/Semi-Supervised Image Classification/Percentage error CIFAR-10, 30 Labels/Image Classification/Percentage error CIFAR-10, 30 Labels/Semi-Supervised Image Classification/Percentage error CIFAR-10, 40 Labels/Image Classification/Percentage error CIFAR-10, 40 Labels/Semi-Supervised Image Classification/Percentage error CIFAR-10, 40% Symmetric Noise/Image Classification/Percentage correct CIFAR-10, 400 Labels (OpenSet, 6/4)/Image Classification/Accuracy CIFAR-10, 400 Labels (OpenSet, 6/4)/Semi-Supervised Image Classification/Accuracy CIFAR-10, 4000 Labels/Image Classification/Percentage error CIFAR-10, 4000 Labels/Semi-Supervised Image Classification/Percentage error CIFAR-10, 50 Labels (OpenSet, 6/4)/Image Classification/Accuracy CIFAR-10, 50 Labels (OpenSet, 6/4)/Semi-Supervised Image Classification/Accuracy CIFAR-10, 500 Labels/Image Classification/Accuracy CIFAR-10, 500 Labels/Image Classification/Accuracy (%)CIFAR-10, 500 Labels/Semi-Supervised Image Classification/Accuracy CIFAR-10, 60% Symmetric Noise/Image Classification/Percentage correct CIFAR-10, 80 Labels/Image Classification/Percentage error CIFAR-10, 80 Labels/Semi-Supervised Image Classification/Percentage error CIFAR-10-LT (ρ=10)/Few-Shot Image Classification/Error Rate CIFAR-10-LT (ρ=10)/Generalized Few-Shot Classification/Error Rate CIFAR-10-LT (ρ=10)/Generalized Few-Shot Learning/Error Rate CIFAR-10-LT (ρ=10)/Image Classification/Error Rate CIFAR-10-LT (ρ=10)/Long-tail Learning/Error Rate CIFAR-10-LT (ρ=100)/Few-Shot Image Classification/Error Rate CIFAR-10-LT (ρ=100)/Generalized Few-Shot Classification/Error Rate CIFAR-10-LT (ρ=100)/Generalized Few-Shot Learning/Error Rate CIFAR-10-LT (ρ=100)/Image Classification/Error Rate CIFAR-10-LT (ρ=100)/Long-tail Learning/Error Rate CIFAR-10-LT (ρ=200)/Few-Shot Image Classification/Error Rate CIFAR-10-LT (ρ=200)/Generalized Few-Shot Classification/Error Rate CIFAR-10-LT (ρ=200)/Generalized Few-Shot Learning/Error Rate CIFAR-10-LT (ρ=200)/Image Classification/Error Rate CIFAR-10-LT (ρ=200)/Long-tail Learning/Error Rate CIFAR-10-LT (ρ=50)/Few-Shot Image Classification/Error Rate CIFAR-10-LT (ρ=50)/Generalized Few-Shot Classification/Error Rate CIFAR-10-LT (ρ=50)/Generalized Few-Shot Learning/Error Rate CIFAR-10-LT (ρ=50)/Image Classification/Error Rate CIFAR-10-LT (ρ=50)/Long-tail Learning/Error Rate CIFAR-100/Adversarial Attack/Attack: AutoAttack CIFAR-100/Adversarial Defense/Accuracy CIFAR-100/Adversarial Defense/autoattack CIFAR-100/Adversarial Robustness/AutoAttacked Accuracy CIFAR-100/Adversarial Robustness/Clean Accuracy CIFAR-100/AutoML/Accuracy (% )CIFAR-100/AutoML/FLOPS CIFAR-100/AutoML/PARAMS CIFAR-100/AutoML/Percentage Error CIFAR-100/AutoML/Search Time (GPU days)CIFAR-100/Class Incremental Learning/Average Accuracy CIFAR-100/Class Incremental Learning/Last Accuracy CIFAR-100/Classification/Accuracy CIFAR-100/Classification/Expected Calibration Error CIFAR-100/Conditional Image Generation/FID CIFAR-100/Conditional Image Generation/Inception Score CIFAR-100/Conditional Image Generation/Intra-FID CIFAR-100/Continual Learning/Average Accuracy CIFAR-100/Continual Learning/Last Accuracy CIFAR-100/Document Text Classification/Test Accuracy CIFAR-100/Federated Learning/ACC@1-100Clients CIFAR-100/Federated Learning/ACC@1-10Clients CIFAR-100/Federated Learning/ACC@1-500 CIFAR-100/Federated Learning/ACC@1-50Clients CIFAR-100/Federated Learning/ACC@5-100Clients CIFAR-100/Image Classification/Accuracy CIFAR-100/Image Classification/All accuracy (10% Labeled)CIFAR-100/Image Classification/All accuracy (50% Labeled)CIFAR-100/Image Classification/Novel accuracy (10% Labeled)CIFAR-100/Image Classification/Novel accuracy (50% Labeled)CIFAR-100/Image Classification/PARAMS CIFAR-100/Image Classification/Percentage correct CIFAR-100/Image Classification/Seen accuracy (10% Labeled)CIFAR-100/Image Classification/Seen accuracy (50% Labeled)CIFAR-100/Image Classification/Test Accuracy CIFAR-100/Image Classification/Top 1 Accuracy CIFAR-100/Image Clustering/ARI CIFAR-100/Image Clustering/Accuracy CIFAR-100/Image Clustering/Backbone CIFAR-100/Image Clustering/NMI CIFAR-100/Image Clustering/Train Set CIFAR-100/Image Generation/FID CIFAR-100/Image Generation/Inception Score CIFAR-100/Image Generation/Intra-FID CIFAR-100/Image Generation/Model Size (MB)CIFAR-100/Knowledge Distillation/Top-1 Accuracy (%)CIFAR-100/Network Pruning/Accuracy CIFAR-100/Network Pruning/GFLOPs CIFAR-100/Network Pruning/Inference Time (ms)CIFAR-100/Neural Architecture Search/Accuracy (% )CIFAR-100/Neural Architecture Search/FLOPS CIFAR-100/Neural Architecture Search/PARAMS CIFAR-100/Neural Architecture Search/Percentage Error CIFAR-100/Neural Architecture Search/Search Time (GPU days)CIFAR-100/Out-of-Distribution Detection/AUROC CIFAR-100/Out-of-Distribution Detection/FPR95 CIFAR-100/Quantization/CIFAR-100 W4A4 Top-1 Accuracy CIFAR-100/Quantization/CIFAR-100 W5A5 Top-1 Accuracy CIFAR-100/Quantization/CIFAR-100 W6A6 Top-1 Accuracy CIFAR-100/Quantization/CIFAR-100 W8A8 Top-1 Accuracy CIFAR-100/Self-Supervised Learning/Top-1 Accuracy CIFAR-100/Semi-Supervised Image Classification/All accuracy (10% Labeled)CIFAR-100/Semi-Supervised Image Classification/All accuracy (50% Labeled)CIFAR-100/Semi-Supervised Image Classification/Novel accuracy (10% Labeled)CIFAR-100/Semi-Supervised Image Classification/Novel accuracy (50% Labeled)CIFAR-100/Semi-Supervised Image Classification/Seen accuracy (10% Labeled)CIFAR-100/Semi-Supervised Image Classification/Seen accuracy (50% Labeled)CIFAR-100/Stochastic Optimization/Accuracy (max)CIFAR-100/Stochastic Optimization/Accuracy (mean)CIFAR-100/Zero-Shot Learning/Accuarcy CIFAR-100/Zero-Shot Learning/Accuracy CIFAR-100 (10000 Labels, ImageNet-100 Unlabeled)/Image Classification/Accuracy CIFAR-100 (10000 Labels, ImageNet-100 Unlabeled)/Semi-Supervised Image Classification/Accuracy CIFAR-100 (250 Labels, ImageNet-100 Unlabeled)/Image Classification/Accuarcy CIFAR-100 (250 Labels, ImageNet-100 Unlabeled)/Semi-Supervised Image Classification/Accuarcy CIFAR-100 (400 Labels, ImageNet-100 Unlabeled)/Image Classification/Accuracy CIFAR-100 (400 Labels, ImageNet-100 Unlabeled)/Semi-Supervised Image Classification/Accuracy CIFAR-100 (alpha=0, 10 clients per round)/Federated Learning/ACC@1-100Clients CIFAR-100 (alpha=0, 20 clients per round)/Federated Learning/ACC@1-100Clients CIFAR-100 (alpha=0, 20 clients per round)/Image Classification/ACC@1-100Clients CIFAR-100 (alpha=0, 5 clients per round)/Federated Learning/ACC@1-100Clients CIFAR-100 (alpha=0.5, 10 clients per round)/Federated Learning/ACC@1-100Clients CIFAR-100 (alpha=0.5, 20 clients per round)/Federated Learning/ACC@1-100Clients CIFAR-100 (alpha=0.5, 5 clients per round)/Federated Learning/ACC@1-100Clients CIFAR-100 (alpha=1000, 10 clients per round)/Federated Learning/ACC@1-100Clients CIFAR-100 (alpha=1000, 20 clients per round)/Federated Learning/ACC@1-100Clients CIFAR-100 (alpha=1000, 5 clients per round)/Federated Learning/ACC@1-100Clients CIFAR-100 (partial ratio 0.01)/Partial Label Learning/Accuracy CIFAR-100 (partial ratio 0.05)/Partial Label Learning/Accuracy CIFAR-100 (partial ratio 0.1)/Partial Label Learning/Accuracy CIFAR-100 - 40 classes + 60 steps of 1 class (Exemplar-free)/Incremental Learning/Average Incremental Accuracy CIFAR-100 - 50 classes + 10 steps of 5 classes/Class Incremental Learning/Final Accuracy CIFAR-100 - 50 classes + 10 steps of 5 classes/Continual Learning/Final Accuracy CIFAR-100 - 50 classes + 10 steps of 5 classes/Incremental Learning/Average Incremental Accuracy CIFAR-100 - 50 classes + 2 steps of 25 classes/Incremental Learning/Average Incremental Accuracy CIFAR-100 - 50 classes + 25 steps of 2 classes/Incremental Learning/Average Incremental Accuracy CIFAR-100 - 50 classes + 5 steps of 10 classes/Class Incremental Learning/Final Accuracy CIFAR-100 - 50 classes + 5 steps of 10 classes/Continual Learning/Final Accuracy CIFAR-100 - 50 classes + 5 steps of 10 classes/Incremental Learning/Average Incremental Accuracy CIFAR-100 - 50 classes + 5 steps of 10 classes/Incremental Learning/Final Accuracy CIFAR-100 - 50 classes + 50 steps of 1 class/Incremental Learning/Average Incremental Accuracy CIFAR-100 AlexNet - 300 Epoch/Continual Learning/Accuracy CIFAR-100 ResNet-18 - 300 Epochs/Continual Learning/Accuracy CIFAR-100 WRN-28-10 - 200 Epochs/Stochastic Optimization/Accuracy CIFAR-100 vs CIFAR-10/Out-of-Distribution Detection/AUPR CIFAR-100 vs CIFAR-10/Out-of-Distribution Detection/AUROC CIFAR-100 vs Gaussian/Out-of-Distribution Detection/AUROC CIFAR-100 vs ImageNet (C)/Out-of-Distribution Detection/AUROC CIFAR-100 vs ImageNet (R)/Out-of-Distribution Detection/AUROC CIFAR-100 vs LSUN (C)/Out-of-Distribution Detection/AUROC CIFAR-100 vs LSUN (R)/Out-of-Distribution Detection/AUROC CIFAR-100 vs SVHN/Out-of-Distribution Detection/AUROC CIFAR-100 vs Uniform/Out-of-Distribution Detection/AUROC CIFAR-100 vs iSUN/Out-of-Distribution Detection/AUROC CIFAR-100, 1000 Labels/Image Classification/Accuracy CIFAR-100, 1000 Labels/Image Classification/Percentage correct CIFAR-100, 1000 Labels/Semi-Supervised Image Classification/Percentage correct CIFAR-100, 200 Labels/Image Classification/Percentage error CIFAR-100, 200 Labels/Semi-Supervised Image Classification/Percentage error CIFAR-100, 2500 Labels/Image Classification/Percentage error CIFAR-100, 2500 Labels/Semi-Supervised Image Classification/Percentage error CIFAR-100, 40% Symmetric Noise/Image Classification/Percentage correct CIFAR-100, 400 Labels/Image Classification/Percentage error CIFAR-100, 400 Labels/Semi-Supervised Image Classification/Percentage error CIFAR-100, 4000 Labels/Image Classification/Accuracy CIFAR-100, 4000 Labels/Semi-Supervised Image Classification/Accuracy CIFAR-100, 5000 Labels/Image Classification/Accuracy (%)CIFAR-100, 5000 Labels/Semi-Supervised Image Classification/Accuracy (%)CIFAR-100, 5000Labels/Image Classification/Percentage correct CIFAR-100, 5000Labels/Semi-Supervised Image Classification/Percentage correct CIFAR-100, 60% Symmetric Noise/Image Classification/Percentage correct CIFAR-100-B0(5steps of 20 classes)/Incremental Learning/Average Incremental Accuracy CIFAR-100-LT (ρ=10)/Few-Shot Image Classification/Error Rate CIFAR-100-LT (ρ=10)/Generalized Few-Shot Classification/Error Rate CIFAR-100-LT (ρ=10)/Generalized Few-Shot Learning/Error Rate CIFAR-100-LT (ρ=10)/Image Classification/Error Rate CIFAR-100-LT (ρ=10)/Long-tail Learning/Error Rate CIFAR-100-LT (ρ=100)/Few-Shot Image Classification/Error Rate CIFAR-100-LT (ρ=100)/Generalized Few-Shot Classification/Error Rate CIFAR-100-LT (ρ=100)/Generalized Few-Shot Learning/Error Rate CIFAR-100-LT (ρ=100)/Image Classification/Error Rate CIFAR-100-LT (ρ=100)/Long-tail Learning/Error Rate CIFAR-100-LT (ρ=200)/Few-Shot Image Classification/Error Rate CIFAR-100-LT (ρ=200)/Generalized Few-Shot Classification/Error Rate CIFAR-100-LT (ρ=200)/Generalized Few-Shot Learning/Error Rate CIFAR-100-LT (ρ=200)/Image Classification/Error Rate CIFAR-100-LT (ρ=200)/Long-tail Learning/Error Rate CIFAR-100-LT (ρ=50)/Few-Shot Image Classification/Error Rate CIFAR-100-LT (ρ=50)/Generalized Few-Shot Classification/Error Rate CIFAR-100-LT (ρ=50)/Generalized Few-Shot Learning/Error Rate CIFAR-100-LT (ρ=50)/Image Classification/Error Rate CIFAR-100-LT (ρ=50)/Long-tail Learning/Error Rate CIFAR-100C/Domain Adaptation/Accuracy CIFAR-100C/Domain Generalization/Accuracy CIFAR-100C/Image Classification/Percentage correct CIFAR-100N/Document Text Classification/Accuracy (mean)CIFAR-100N/Image Classification/Accuracy (mean)CIFAR-10C/Classification/Accuracy on Brightness Corrupted Images CIFAR-10C/Domain Adaptation/Accuracy CIFAR-10C/Domain Generalization/Accuracy CIFAR-10N/Document Text Classification/Accuracy CIFAR-10N/Image Classification/Accuracy CIFAR-10N-Aggregate/Document Text Classification/Accuracy (mean)CIFAR-10N-Aggregate/Image Classification/Accuracy (mean)CIFAR-10N-Random1/Document Text Classification/Accuracy (mean)CIFAR-10N-Random1/Image Classification/Accuracy (mean)CIFAR-10N-Random2/Document Text Classification/Accuracy (mean)CIFAR-10N-Random2/Image Classification/Accuracy (mean)CIFAR-10N-Random3/Document Text Classification/Accuracy (mean)CIFAR-10N-Random3/Image Classification/Accuracy (mean)CIFAR-10N-Worst/Document Text Classification/Accuracy (mean)CIFAR-10N-Worst/Image Classification/Accuracy (mean)cifar-10, 10 Labels/Image Classification/Accuracy (Test)cifar-10, 10 Labels/Semi-Supervised Image Classification/Accuracy (Test)cifar-10,4000/Image Classification/Percentage error cifar-100, 10000 Labels/Image Classification/Percentage error cifar-100, 10000 Labels/Semi-Supervised Image Classification/Percentage error

Statistics

Papers: 16,145
Benchmarks: 89

Links

Tasks

2D Classification Active Learning Adversarial Attack Adversarial Defense Adversarial Robustness Anomaly Detection AutoML Binarization Classification Classification with Binary Neural Network Classification with Binary Weight Network Clean-label Backdoor Attack (0.05%)Conditional Image Generation Continual Learning Contrastive Learning Data Augmentation Dataset Distillation - 1IPC Density Estimation Document Text Classification Domain-IL Continual Learning Federated Learning Graph Classification Hard-label Attack Image Classification Image Classification with Human Noise Image Classification with Label Noise Image Clustering Image Compression Image Generation Image Retrieval Learning with noisy labels Long-tail Learning Long-tail Learning on CIFAR-10-LT (ρ=100)Model Compression Model Poisoning Nature-Inspired Optimization Algorithm Network Pruning Neural Architecture Search Neural Network Compression Novel Class Discovery Object Recognition Online Clustering Open-World Semi-Supervised Learning Out of Distribution (OOD) Detection Out-of-Distribution Detection Partial Label Learning Personalized Federated Learning Provable Adversarial Defense Quantization ROLSSL-Consistent ROLSSL-Reversed ROLSSL-Uniform Robust classification Self-Supervised Learning Semi-Supervised Image Classification Semi-Supervised Image Classification (Cold Start)Sequential Image Classification Small Data Image Classification Sparse Learning and binarization Stochastic Optimization Supervised Image Retrieval Transductive Zero-Shot Classification Unsupervised Anomaly Detection Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly Unsupervised Anomaly Detection with Specified Settings -- 1% anomaly Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly Unsupervised Anomaly Detection with Specified Settings -- 30% anomaly Unsupervised Image Classification Zero-Shot Learning