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Models/CODE-CL

CODE-CL

Reported on 8 benchmarks across 1 task · 1 paper · 7 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology8 results

Continual LearningonPermuted MNIST
BWT· 2024-11-21
-0.24
SOTA
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual Learning arXiv:2411.15235
Continual Learningonsplit CIFAR-100
Average Accuracy· 2024-11-21
77.21
SOTA
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual Learning arXiv:2411.15235
Continual Learningonsplit CIFAR-100
BWT· 2024-11-21
-1.1
SOTA
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual Learning arXiv:2411.15235
Continual LearningonminiImagenet
Average Accuracy· 2024-11-21
68.83
SOTA
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual Learning arXiv:2411.15235
Continual LearningonminiImagenet
BWT· 2024-11-21
-1.1
SOTA
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual Learning arXiv:2411.15235
Continual Learningon5-Datasets
Average Accuracy· 2024-11-21
93.32
SOTA
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual Learning arXiv:2411.15235
Continual Learningon5-Datasets
BWT· 2024-11-21
-0.25
SOTA
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual Learning arXiv:2411.15235
Continual LearningonPermuted MNIST
Average Accuracy· 2024-11-21
96.56
best: 97.988 (RMN)
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual Learning arXiv:2411.15235