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Incremental Learning On Cifar 100 50 Classes 2

Métriques

Average Incremental Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Average Incremental Accuracy	Paper Title	Repository
FOSTER	67.95	FOSTER: Feature Boosting and Compression for Class-Incremental Learning	-
BiC	53.21	Large Scale Incremental Learning	-
TCIL-Lite	73.50	Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning	-
RMM (Modified ResNet-32)	67.61	RMM: Reinforced Memory Management for Class-Incremental Learning	-
PODNet (CNN)	63.19	PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning	-
D3Former	70.94	D3Former: Debiased Dual Distilled Transformer for Incremental Learning	-
DER(Standard ResNet-18)	72.45	DER: Dynamically Expandable Representation for Class Incremental Learning	-
iCaRL*	52.57	iCaRL: Incremental Classifier and Representation Learning	-
TCIL	73.72	Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning	-
UCIR (CNN)*	60.18	Learning a Unified Classifier Incrementally via Rebalancing
UCIR (NME)*	60.12	Learning a Unified Classifier Incrementally via Rebalancing
CCIL-SD	65.86	Essentials for Class Incremental Learning	-
DER(Modified ResNet-32）	66.36	DER: Dynamically Expandable Representation for Class Incremental Learning	-

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Incremental Learning On Cifar 100 50 Classes 2 | SOTA | HyperAI