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Système

Incremental Learning On Imagenet 10 Steps

Métriques

# M Params

Average Incremental Accuracy

Average Incremental Accuracy Top-5

Final Accuracy

Final Accuracy Top-5

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	# M Params	Average Incremental Accuracy	Average Incremental Accuracy Top-5	Final Accuracy	Final Accuracy Top-5	Paper Title	Repository
DER w/o Pruning	116.89	68.84	88.17	60.16	82.86	DER: Dynamically Expandable Representation for Class Incremental Learning
DyTox	11.36	71.29	88.59	63.34	84.49	DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion
E2E	11.68	-	72.09	-	52.29	End-to-End Incremental Learning
WA	11.68	65.67	86.60	55.60	81.10	Maintaining Discrimination and Fairness in Class Incremental Learning	-
DER	-	66.73	87.08	58.62	81.89	DER: Dynamically Expandable Representation for Class Incremental Learning
RMM (ResNet-18)	-	67.45	-	-	-	RMM: Reinforced Memory Management for Class-Incremental Learning
BiC	11.68	-	84.00	-	73.20	Large Scale Incremental Learning
FOSTER	-	68.34	-	-	-	FOSTER: Feature Boosting and Compression for Class-Incremental Learning
kNN-CLIP	-	85.5	-	-	-	Revisiting a kNN-based Image Classification System with High-capacity Storage	-
iCaRL	11.68	38.40	63.70	22.70	44.00	iCaRL: Incremental Classifier and Representation Learning

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