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Datasets/CIFAR-100 (alpha=0.5, 20 clients per round)

CIFAR-100 (alpha=0.5, 20 clients per round)

Benchmarks

Federated Learning/ACC@1-100Clients

Related Benchmarks

CIFAR-100/Adversarial Attack/Attack: AutoAttackCIFAR-100/Adversarial Defense/AccuracyCIFAR-100/Adversarial Defense/autoattackCIFAR-100/Adversarial Robustness/AutoAttacked AccuracyCIFAR-100/Adversarial Robustness/Clean AccuracyCIFAR-100/AutoML/Accuracy (% )CIFAR-100/AutoML/FLOPSCIFAR-100/AutoML/PARAMSCIFAR-100/AutoML/Percentage ErrorCIFAR-100/AutoML/Search Time (GPU days)CIFAR-100/Class Incremental Learning/Average AccuracyCIFAR-100/Class Incremental Learning/Last AccuracyCIFAR-100/Classification/AccuracyCIFAR-100/Classification/Expected Calibration ErrorCIFAR-100/Conditional Image Generation/FIDCIFAR-100/Conditional Image Generation/Inception ScoreCIFAR-100/Conditional Image Generation/Intra-FIDCIFAR-100/Continual Learning/Average AccuracyCIFAR-100/Continual Learning/Last AccuracyCIFAR-100/Document Text Classification/Test AccuracyCIFAR-100/Federated Learning/ACC@1-100ClientsCIFAR-100/Federated Learning/ACC@1-10ClientsCIFAR-100/Federated Learning/ACC@1-500CIFAR-100/Federated Learning/ACC@1-50ClientsCIFAR-100/Federated Learning/ACC@5-100ClientsCIFAR-100/Image Classification/AccuracyCIFAR-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/PARAMSCIFAR-100/Image Classification/Percentage correctCIFAR-100/Image Classification/Seen accuracy (10% Labeled)CIFAR-100/Image Classification/Seen accuracy (50% Labeled)CIFAR-100/Image Classification/Test AccuracyCIFAR-100/Image Classification/Top 1 AccuracyCIFAR-100/Image Clustering/ARICIFAR-100/Image Clustering/AccuracyCIFAR-100/Image Clustering/BackboneCIFAR-100/Image Clustering/NMICIFAR-100/Image Clustering/Train SetCIFAR-100/Image Generation/FIDCIFAR-100/Image Generation/Inception ScoreCIFAR-100/Image Generation/Intra-FIDCIFAR-100/Image Generation/Model Size (MB)CIFAR-100/Knowledge Distillation/Top-1 Accuracy (%)CIFAR-100/Network Pruning/AccuracyCIFAR-100/Network Pruning/GFLOPsCIFAR-100/Network Pruning/Inference Time (ms)CIFAR-100/Neural Architecture Search/Accuracy (% )CIFAR-100/Neural Architecture Search/FLOPSCIFAR-100/Neural Architecture Search/PARAMSCIFAR-100/Neural Architecture Search/Percentage ErrorCIFAR-100/Neural Architecture Search/Search Time (GPU days)CIFAR-100/Out-of-Distribution Detection/AUROCCIFAR-100/Out-of-Distribution Detection/FPR95CIFAR-100/Quantization/CIFAR-100 W4A4 Top-1 AccuracyCIFAR-100/Quantization/CIFAR-100 W5A5 Top-1 AccuracyCIFAR-100/Quantization/CIFAR-100 W6A6 Top-1 AccuracyCIFAR-100/Quantization/CIFAR-100 W8A8 Top-1 AccuracyCIFAR-100/Self-Supervised Learning/Top-1 AccuracyCIFAR-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/AccuarcyCIFAR-100/Zero-Shot Learning/AccuracyCIFAR-100 (10000 Labels, ImageNet-100 Unlabeled)/Image Classification/AccuracyCIFAR-100 (10000 Labels, ImageNet-100 Unlabeled)/Semi-Supervised Image Classification/AccuracyCIFAR-100 (250 Labels, ImageNet-100 Unlabeled)/Image Classification/AccuarcyCIFAR-100 (250 Labels, ImageNet-100 Unlabeled)/Semi-Supervised Image Classification/AccuarcyCIFAR-100 (400 Labels, ImageNet-100 Unlabeled)/Image Classification/AccuracyCIFAR-100 (400 Labels, ImageNet-100 Unlabeled)/Semi-Supervised Image Classification/AccuracyCIFAR-100 (alpha=0, 10 clients per round)/Federated Learning/ACC@1-100ClientsCIFAR-100 (alpha=0, 20 clients per round)/Federated Learning/ACC@1-100ClientsCIFAR-100 (alpha=0, 20 clients per round)/Image Classification/ACC@1-100ClientsCIFAR-100 (alpha=0, 5 clients per round)/Federated Learning/ACC@1-100ClientsCIFAR-100 (alpha=0.5, 10 clients per round)/Federated Learning/ACC@1-100ClientsCIFAR-100 (alpha=0.5, 5 clients per round)/Federated Learning/ACC@1-100ClientsCIFAR-100 (alpha=1000, 10 clients per round)/Federated Learning/ACC@1-100ClientsCIFAR-100 (alpha=1000, 20 clients per round)/Federated Learning/ACC@1-100ClientsCIFAR-100 (alpha=1000, 5 clients per round)/Federated Learning/ACC@1-100ClientsCIFAR-100 (partial ratio 0.01)/Partial Label Learning/AccuracyCIFAR-100 (partial ratio 0.05)/Partial Label Learning/AccuracyCIFAR-100 (partial ratio 0.1)/Partial Label Learning/AccuracyCIFAR-100 - 40 classes + 60 steps of 1 class (Exemplar-free)/Incremental Learning/Average Incremental AccuracyCIFAR-100 - 50 classes + 10 steps of 5 classes/Class Incremental Learning/Final AccuracyCIFAR-100 - 50 classes + 10 steps of 5 classes/Continual Learning/Final AccuracyCIFAR-100 - 50 classes + 10 steps of 5 classes/Incremental Learning/Average Incremental AccuracyCIFAR-100 - 50 classes + 2 steps of 25 classes/Incremental Learning/Average Incremental AccuracyCIFAR-100 - 50 classes + 25 steps of 2 classes/Incremental Learning/Average Incremental AccuracyCIFAR-100 - 50 classes + 5 steps of 10 classes/Class Incremental Learning/Final AccuracyCIFAR-100 - 50 classes + 5 steps of 10 classes/Continual Learning/Final AccuracyCIFAR-100 - 50 classes + 5 steps of 10 classes/Incremental Learning/Average Incremental AccuracyCIFAR-100 - 50 classes + 5 steps of 10 classes/Incremental Learning/Final AccuracyCIFAR-100 - 50 classes + 50 steps of 1 class/Incremental Learning/Average Incremental AccuracyCIFAR-100 AlexNet - 300 Epoch/Continual Learning/AccuracyCIFAR-100 ResNet-18 - 300 Epochs/Continual Learning/AccuracyCIFAR-100 WRN-28-10 - 200 Epochs/Stochastic Optimization/AccuracyCIFAR-100 vs CIFAR-10/Out-of-Distribution Detection/AUPRCIFAR-100 vs CIFAR-10/Out-of-Distribution Detection/AUROCCIFAR-100 vs Gaussian/Out-of-Distribution Detection/AUROCCIFAR-100 vs ImageNet (C)/Out-of-Distribution Detection/AUROCCIFAR-100 vs ImageNet (R)/Out-of-Distribution Detection/AUROCCIFAR-100 vs LSUN (C)/Out-of-Distribution Detection/AUROCCIFAR-100 vs LSUN (R)/Out-of-Distribution Detection/AUROCCIFAR-100 vs SVHN/Out-of-Distribution Detection/AUROCCIFAR-100 vs Uniform/Out-of-Distribution Detection/AUROCCIFAR-100 vs iSUN/Out-of-Distribution Detection/AUROCCIFAR-100, 1000 Labels/Image Classification/AccuracyCIFAR-100, 1000 Labels/Image Classification/Percentage correctCIFAR-100, 1000 Labels/Semi-Supervised Image Classification/Percentage correctCIFAR-100, 200 Labels/Image Classification/Percentage errorCIFAR-100, 200 Labels/Semi-Supervised Image Classification/Percentage errorCIFAR-100, 2500 Labels/Image Classification/Percentage errorCIFAR-100, 2500 Labels/Semi-Supervised Image Classification/Percentage errorCIFAR-100, 40% Symmetric Noise/Image Classification/Percentage correctCIFAR-100, 400 Labels/Image Classification/Percentage errorCIFAR-100, 400 Labels/Semi-Supervised Image Classification/Percentage errorCIFAR-100, 4000 Labels/Image Classification/AccuracyCIFAR-100, 4000 Labels/Semi-Supervised Image Classification/AccuracyCIFAR-100, 5000 Labels/Image Classification/Accuracy (%)CIFAR-100, 5000 Labels/Semi-Supervised Image Classification/Accuracy (%)CIFAR-100, 5000Labels/Image Classification/Percentage correctCIFAR-100, 5000Labels/Semi-Supervised Image Classification/Percentage correctCIFAR-100, 60% Symmetric Noise/Image Classification/Percentage correctCIFAR-100-B0(5steps of 20 classes)/Incremental Learning/Average Incremental AccuracyCIFAR-100-LT (ρ=10)/Few-Shot Image Classification/Error RateCIFAR-100-LT (ρ=10)/Generalized Few-Shot Classification/Error RateCIFAR-100-LT (ρ=10)/Generalized Few-Shot Learning/Error RateCIFAR-100-LT (ρ=10)/Image Classification/Error RateCIFAR-100-LT (ρ=10)/Long-tail Learning/Error RateCIFAR-100-LT (ρ=100)/Few-Shot Image Classification/Error RateCIFAR-100-LT (ρ=100)/Generalized Few-Shot Classification/Error RateCIFAR-100-LT (ρ=100)/Generalized Few-Shot Learning/Error RateCIFAR-100-LT (ρ=100)/Image Classification/Error RateCIFAR-100-LT (ρ=100)/Long-tail Learning/Error RateCIFAR-100-LT (ρ=200)/Few-Shot Image Classification/Error RateCIFAR-100-LT (ρ=200)/Generalized Few-Shot Classification/Error RateCIFAR-100-LT (ρ=200)/Generalized Few-Shot Learning/Error RateCIFAR-100-LT (ρ=200)/Image Classification/Error RateCIFAR-100-LT (ρ=200)/Long-tail Learning/Error RateCIFAR-100-LT (ρ=50)/Few-Shot Image Classification/Error RateCIFAR-100-LT (ρ=50)/Generalized Few-Shot Classification/Error RateCIFAR-100-LT (ρ=50)/Generalized Few-Shot Learning/Error RateCIFAR-100-LT (ρ=50)/Image Classification/Error RateCIFAR-100-LT (ρ=50)/Long-tail Learning/Error RateCIFAR-100C/Domain Adaptation/AccuracyCIFAR-100C/Domain Generalization/AccuracyCIFAR-100C/Image Classification/Percentage correctCIFAR-100N/Document Text Classification/Accuracy (mean)CIFAR-100N/Image Classification/Accuracy (mean)cifar-100, 10000 Labels/Image Classification/Percentage errorcifar-100, 10000 Labels/Semi-Supervised Image Classification/Percentage error

Statistics

Papers
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Benchmarks
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Tasks

Federated Learning