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SotA/Methodology/Incremental Learning/CIFAR-100 - 50 classes + 10 steps of 5 classes

Incremental Learning on CIFAR-100 - 50 classes + 10 steps of 5 classes

Metric: Average Incremental Accuracy (higher is better)

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#Model↕Average Incremental Accuracy▼AugmentationsPaperDate↕Code
1TCIL73.72NoResolving Task Confusion in Dynamic Expansion Ar...2022-12-29Code
2TCIL-Lite73.5NoResolving Task Confusion in Dynamic Expansion Ar...2022-12-29Code
3DER(Standard ResNet-18)72.45NoDER: Dynamically Expandable Representation for C...2021-03-31Code
4D3Former70.94NoD3Former: Debiased Dual Distilled Transformer fo...2022-07-25Code
5FOSTER67.95NoFOSTER: Feature Boosting and Compression for Cla...2022-04-10Code
6RMM (Modified ResNet-32)67.61NoRMM: Reinforced Memory Management for Class-Incr...2023-01-14Code
7DER(Modified ResNet-32）66.36NoDER: Dynamically Expandable Representation for C...2021-03-31Code
8CCIL-SD65.86NoEssentials for Class Incremental Learning2021-02-18Code
9PODNet (CNN)63.19NoPODNet: Pooled Outputs Distillation for Small-Ta...2020-04-28Code
10UCIR (CNN)*60.18No--Code
11UCIR (NME)*60.12No--Code
12BiC53.21NoLarge Scale Incremental Learning2019-05-30Code
13iCaRL*52.57NoiCaRL: Incremental Classifier and Representation...2016-11-23Code