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  4. Incremental Learning On Cifar 100 50 Classes 2

Incremental Learning On Cifar 100 50 Classes 2

评估指标

Average Incremental Accuracy

评测结果

各个模型在此基准测试上的表现结果

模型名称
Average Incremental Accuracy
Paper TitleRepository
FOSTER67.95FOSTER: Feature Boosting and Compression for Class-Incremental Learning-
BiC53.21Large Scale Incremental Learning-
TCIL-Lite73.50Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning-
RMM (Modified ResNet-32)67.61RMM: Reinforced Memory Management for Class-Incremental Learning-
PODNet (CNN)63.19PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning-
D3Former70.94D3Former: Debiased Dual Distilled Transformer for Incremental Learning-
DER(Standard ResNet-18)72.45DER: Dynamically Expandable Representation for Class Incremental Learning-
iCaRL*52.57iCaRL: Incremental Classifier and Representation Learning-
TCIL73.72Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning-
UCIR (CNN)*60.18Learning a Unified Classifier Incrementally via Rebalancing
UCIR (NME)*60.12Learning a Unified Classifier Incrementally via Rebalancing
CCIL-SD65.86Essentials for Class Incremental Learning-
DER(Modified ResNet-32)66.36DER: Dynamically Expandable Representation for Class Incremental Learning-
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