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Few Shot Class Incremental Learning On Mini
Metrics
Average Accuracy
Last Accuracy
Results
Performance results of various models on this benchmark
| Paper Title | Repository | |||
|---|---|---|---|---|
| PriViLege | 95.27 | 94.10 | Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners | |
| SV-T | 85.07 | 81.65 | Semantic-visual Guided Transformer for Few-shot Class-incremental Learning | - |
| BOT | - | 59.57 | A Bag of Tricks for Few-Shot Class-Incremental Learning | - |
| NC-FSCIL | 67.82 | 58.31 | Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class-Incremental Learning | - |
| C-FSCIL | 61.61 | 51.41 | Constrained Few-shot Class-incremental Learning | |
| FACT | 60.70 | 50.49 | Forward Compatible Few-Shot Class-Incremental Learning | |
| LIMIT | 59.06 | 49.19 | Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks | |
| F2M | 54.89 | 47.84 | Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima | |
| CEC | 57.74 | 47.63 | Few-Shot Incremental Learning with Continually Evolved Classifiers | |
| IDLVQ-C | 51.16 | 41.84 | Incremental few-shot learning via vector quantization in deep embedded space | - |
| AL-MML | 39.64 | 24.42 | Few-Shot Class-Incremental Learning |
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