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Models/NAPA-VQ

NAPA-VQ

Reported on 18 benchmarks across 2 tasks · 1 paper · 18 SOTA

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

Methodology18 results

Continual LearningonImageNetSubset
Average accuracy - 5 tasks· 2023-08-18
69.15
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Continual LearningonImageNetSubset
average accuracy - 10 tasks· 2023-08-18
68.83
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Continual LearningonImageNetSubset
average accuracy - 20 tasks· 2023-08-18
63.09
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Continual Learningoncifar100
Average accuracy - 5 tasks· 2023-08-18
70.44
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Continual Learningoncifar100
average accuracy - 10 tasks· 2023-08-18
69.04
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Continual Learningoncifar100
average accuracy - 20 tasks· 2023-08-18
67.42
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Continual LearningonTinyImageNet
Average accuracy - 5 tasks· 2023-08-18
52.77
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Continual LearningonTinyImageNet
average accuracy - 10 tasks· 2023-08-18
51.78
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Continual LearningonTinyImageNet
average accuracy - 20 tasks· 2023-08-18
49.51
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Class Incremental LearningonImageNetSubset
Average accuracy - 5 tasks· 2023-08-18
69.15
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Class Incremental LearningonImageNetSubset
average accuracy - 10 tasks· 2023-08-18
68.83
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Class Incremental LearningonImageNetSubset
average accuracy - 20 tasks· 2023-08-18
63.09
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Class Incremental Learningoncifar100
Average accuracy - 5 tasks· 2023-08-18
70.44
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Class Incremental Learningoncifar100
average accuracy - 10 tasks· 2023-08-18
69.04
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Class Incremental Learningoncifar100
average accuracy - 20 tasks· 2023-08-18
67.42
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Class Incremental LearningonTinyImageNet
Average accuracy - 5 tasks· 2023-08-18
52.77
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Class Incremental LearningonTinyImageNet
average accuracy - 10 tasks· 2023-08-18
51.78
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297
Class Incremental LearningonTinyImageNet
average accuracy - 20 tasks· 2023-08-18
49.51
SOTA
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning arXiv:2308.09297