Update README.md

README.md

@@ -54,7 +54,7 @@ Traditional machine learning systems are deployed under the closed-world setting
 - [x] `PASS`: Prototype Augmentation and Self-Supervision for Incremental Learning. CVPR2021 [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Prototype_Augmentation_and_Self-Supervision_for_Incremental_Learning_CVPR_2021_paper.pdf)]
 - [x] `RMM`: RMM: Reinforced Memory Management for Class-Incremental Learning. NeurIPS2021 [[paper](https://proceedings.neurips.cc/paper/2021/hash/1cbcaa5abbb6b70f378a3a03d0c26386-Abstract.html)]
 - [x] `IL2A`: Class-Incremental Learning via Dual Augmentation. NeurIPS2021 [[paper](https://proceedings.neurips.cc/paper/2021/file/77ee3bc58ce560b86c2b59363281e914-Paper.pdf)]
-- [ ] `SSRE`: Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning. CVPR2022 [[paper](https://arxiv.org/abs/2203.06359)]
+- [x] `SSRE`: Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning. CVPR2022 [[paper](https://arxiv.org/abs/2203.06359)]
 - [X] `FeTrIL`: Feature Translation for Exemplar-Free Class-Incremental Learning. WACV2023 [[paper](https://arxiv.org/abs/2211.13131)]
 - [X] `Coil`: Co-Transport for Class-Incremental Learning. ACM MM2021 [[paper](https://arxiv.org/abs/2107.12654)]
 - [X] `FOSTER`: Feature Boosting and Compression for Class-incremental Learning. ECCV 2022 [[paper](https://arxiv.org/abs/2204.04662)]