Image Classification on CIFAR-100 LT IF=10
65.5Top-1 AccuracyProCo
Evaluation Results
| Method | Links | |
|---|---|---|
| ProCoBackbone=ResNet-32, Training Epochs=2002024.03 | 65.5 | |
| BCLBackbone=ResNet-32, Training Epochs=2002024.03 | 64.6 | |
| Logit AdjustmentBackbone=ResNet-32, Training Epochs=2002024.03 | 64 | |
| Hybrid-SCBackbone=ResNet-32, Training Epochs=2002024.03 | 63.1 | |
| ResLTBackbone=ResNet-32, Training Epochs=2002024.03 | 62 | |
| MetaSAug-LDAMBackbone=ResNet-32, Training Epochs=2002024.03 | 61.3 | |
| Casual ModelBackbone=ResNet-32, Training Epochs=2002024.03 | 59.6 | |
| BBNBackbone=ResNet-32, Training Epochs=2002024.03 | 59.1 | |
| TSCBackbone=ResNet-32, Training Epochs=2002024.03 | 59 | |
| SSPBackbone=ResNet-32, Training Epochs=2002024.03 | 58.9 | |
| LDAM-DRWBackbone=ResNet-32, Training Epochs=2002024.03 | 58.7 | |
| CB-FocalBackbone=ResNet-32, Training Epochs=2002024.03 | 58 | |
| MLLM-LLaVA-FLType=Our framework2024.09 | 48.87 | |
| CLIP2FLType=SOTA2024.09 | 48.2 | |
| CReFFType=Classifier-retraining2024.09 | 47.08 | |
| CCVRType=Heterogeneity-oriented FL methods2024.09 | 46.88 | |
| Ratio LossType=Imbalance-oriented FL methods2024.09 | 46.79 | |
| FedNovaType=Heterogeneity-oriented FL methods2024.09 | 46.75 | |
| FedBEType=Heterogeneity-oriented FL methods2024.09 | 46.25 | |
| FedDFType=Heterogeneity-oriented FL methods2024.09 | 46.19 | |
| FedProxType=Heterogeneity-oriented FL methods2024.09 | 46.1 | |
| FedAvgType=Heterogeneity-oriented FL methods2024.09 | 45.87 | |
| Fed-Focal LossType=Imbalance-oriented FL methods2024.09 | 45.52 | |
| FedAvgMType=Heterogeneity-oriented FL methods2024.09 | 44.66 | |
| FedAvg+ τ-normType=Imbalance-oriented FL methods2024.09 | 43.65 |