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Models/LIMIT

LIMIT

Reported on 8 benchmarks across 2 tasks · 1 paper

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

Methodology8 results

Continual LearningonCIFAR-100
Average Accuracy· 2022-03-31
61.85
best: 88.08 (PriViLege)
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks arXiv:2203.17030
Continual LearningonCIFAR-100
Last Accuracy· 2022-03-31
51.23
best: 86.06 (PriViLege)
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks arXiv:2203.17030
Continual Learningonmini-Imagenet
Average Accuracy· 2022-03-31
59.06
best: 95.27 (PriViLege)
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks arXiv:2203.17030
Continual Learningonmini-Imagenet
Last Accuracy · 2022-03-31
49.19
best: 96.24 (CoACT)
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks arXiv:2203.17030
Class Incremental LearningonCIFAR-100
Average Accuracy· 2022-03-31
61.85
best: 88.08 (PriViLege)
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks arXiv:2203.17030
Class Incremental LearningonCIFAR-100
Last Accuracy· 2022-03-31
51.23
best: 86.06 (PriViLege)
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks arXiv:2203.17030
Class Incremental Learningonmini-Imagenet
Average Accuracy· 2022-03-31
59.06
best: 95.27 (PriViLege)
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks arXiv:2203.17030
Class Incremental Learningonmini-Imagenet
Last Accuracy · 2022-03-31
49.19
best: 96.24 (CoACT)
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks arXiv:2203.17030