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Models/PL-CFE

PL-CFE

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.

Computer Vision8 results

Image ClassificationonTiered ImageNet 5-way (5-shot)
Accuracy· 2022-09-27
64.31
best: 98.8 (CAML [Laion-2b])
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning arXiv:2209.13635
Image ClassificationonMini-Imagenet 5-way (1-shot)
Accuracy· 2022-09-27
49.13
best: 97.95 (SgVA-CLIP)
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning arXiv:2209.13635
Image ClassificationonTiered ImageNet 5-way (1-shot)
Accuracy· 2022-09-27
49.51
best: 96.8 (CAML [Laion-2b])
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning arXiv:2209.13635
Image ClassificationonMini-Imagenet 5-way (5-shot)
Accuracy· 2022-09-27
62.91
best: 98.72 (SgVA-CLIP)
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning arXiv:2209.13635
Few-Shot Image ClassificationonTiered ImageNet 5-way (5-shot)
Accuracy· 2022-09-27
64.31
best: 98.8 (CAML [Laion-2b])
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning arXiv:2209.13635
Few-Shot Image ClassificationonMini-Imagenet 5-way (1-shot)
Accuracy· 2022-09-27
49.13
best: 97.95 (SgVA-CLIP)
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning arXiv:2209.13635
Few-Shot Image ClassificationonTiered ImageNet 5-way (1-shot)
Accuracy· 2022-09-27
49.51
best: 96.8 (CAML [Laion-2b])
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning arXiv:2209.13635
Few-Shot Image ClassificationonMini-Imagenet 5-way (5-shot)
Accuracy· 2022-09-27
62.91
best: 98.72 (SgVA-CLIP)
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning arXiv:2209.13635