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Models/PT+MAP

PT+MAP

Reported on 18 benchmarks across 2 tasks · 1 paper · 16 SOTA

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

Computer Vision18 results

Image ClassificationonCUB 200 5-way 5-shot
Accuracy· uses extra data· 2020-06-06
93.99
best: 98.7 (CAML [Laion-2b])
SOTA
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Image ClassificationonMini-ImageNet-CUB 5-way (5-shot)
Accuracy· 2020-06-06
76.51
best: 80.74 (TRIDENT)
SOTA
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Image ClassificationonCIFAR-FS 5-way (1-shot)
Accuracy· 2020-06-06
87.69
best: 89.94 (PT+MAP+SF+SOT (transductive))
SOTA
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Image ClassificationonMini-ImageNet-CUB 5-way (1-shot)
Accuracy· 2020-06-06
62.49
best: 84.61 (TRIDENT)
SOTA
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Image ClassificationonMini-Imagenet 5-way (10-shot)
Accuracy· 2020-06-06
90.03
SOTA
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Image ClassificationonTiered ImageNet 5-way (1-shot)
Accuracy· 2020-06-06
85.41
best: 96.8 (CAML [Laion-2b])
SOTA
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Image ClassificationonTiered ImageNet 5-way (5-shot)
Accuracy· 2020-06-06
90.44
best: 98.8 (CAML [Laion-2b])
SOTA
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Image ClassificationonCIFAR-FS 5-way (5-shot)
Accuracy· 2020-06-06
90.68
best: 93.5 (CAML [Laion-2b])
SOTA
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Few-Shot Image ClassificationonCUB 200 5-way 5-shot
Accuracy· uses extra data· 2020-06-06
93.99
best: 98.7 (CAML [Laion-2b])
SOTA
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Few-Shot Image ClassificationonMini-ImageNet-CUB 5-way (5-shot)
Accuracy· 2020-06-06
76.51
best: 80.74 (TRIDENT)
SOTA
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Few-Shot Image ClassificationonCIFAR-FS 5-way (1-shot)
Accuracy· 2020-06-06
87.69
best: 89.94 (PT+MAP+SF+SOT (transductive))
SOTA
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Few-Shot Image ClassificationonMini-ImageNet-CUB 5-way (1-shot)
Accuracy· 2020-06-06
62.49
best: 84.61 (TRIDENT)
SOTA
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Few-Shot Image ClassificationonMini-Imagenet 5-way (10-shot)
Accuracy· 2020-06-06
90.03
SOTA
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Few-Shot Image ClassificationonTiered ImageNet 5-way (1-shot)
Accuracy· 2020-06-06
85.41
best: 96.8 (CAML [Laion-2b])
SOTA
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Few-Shot Image ClassificationonTiered ImageNet 5-way (5-shot)
Accuracy· 2020-06-06
90.44
best: 98.8 (CAML [Laion-2b])
SOTA
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Few-Shot Image ClassificationonCIFAR-FS 5-way (5-shot)
Accuracy· 2020-06-06
90.68
best: 93.5 (CAML [Laion-2b])
SOTA
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Image ClassificationonMini-Imagenet 5-way (5-shot)
Accuracy· 2020-06-06
88.82
best: 98.72 (SgVA-CLIP)
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806
Few-Shot Image ClassificationonMini-Imagenet 5-way (5-shot)
Accuracy· 2020-06-06
88.82
best: 98.72 (SgVA-CLIP)
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning arXiv:2006.03806