Incremental Learning On Cifar 100 B0 5Steps

Metrics

Average Incremental Accuracy

Results

Performance results of various models on this benchmark

		Paper Title
TCIL	77.72	Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning
TCIL-Lite	76.96	Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning
DER(w/o P)	76.80	DER: Dynamically Expandable Representation for Class Incremental Learning
BiC	73.10	Large Scale Incremental Learning
WA	72.81	Maintaining Discrimination and Fairness in Class Incremental Learning
iCaRL	71.14	iCaRL: Incremental Classifier and Representation Learning
RPSNet	70.50	An Adaptive Random Path Selection Approach for Incremental Learning
PODNet	66.70	PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning
UCIR	62.77	Learning a Unified Classifier Incrementally via Rebalancing

0 of 9 row(s) selected.

Incremental Learning On Cifar 100 B0 5Steps

Metrics

Average Incremental Accuracy

Results

Performance results of various models on this benchmark

		Paper Title
TCIL	77.72	Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning
TCIL-Lite	76.96	Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning
DER(w/o P)	76.80	DER: Dynamically Expandable Representation for Class Incremental Learning
BiC	73.10	Large Scale Incremental Learning
WA	72.81	Maintaining Discrimination and Fairness in Class Incremental Learning
iCaRL	71.14	iCaRL: Incremental Classifier and Representation Learning
RPSNet	70.50	An Adaptive Random Path Selection Approach for Incremental Learning
PODNet	66.70	PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning
UCIR	62.77	Learning a Unified Classifier Incrementally via Rebalancing

0 of 9 row(s) selected.

Incremental Learning On Cifar 100 B0 5Steps | SOTA | HyperAI