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Models/EnAET

EnAET

Reported on 22 benchmarks across 2 tasks · 2 papers · 13 SOTA

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

Computer Vision22 results

Image ClassificationonSTL-10
Percentage correct· uses extra data· 2019-11-21
95.48
best: 99.64 (µ2Net+ (ViT-L/16))
SOTA
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Image ClassificationonCIFAR-10, 4000 Labels
Percentage error· 2019-11-21
4.18
best: 3.96 (SimMatch)
SOTA
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Image ClassificationonCIFAR-100, 1000 Labels
Percentage correct· 2019-11-21
41.27
SOTA
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Image ClassificationonSTL-10
Accuracy· 2019-11-21
95.48
best: 99.7 (TURTLE (CLIP + DINOv2))
SOTA
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Image ClassificationonSVHN, 1000 labels
Accuracy· 2019-11-21
97.58
SOTA
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Image ClassificationonCIFAR-100, 5000Labels
Percentage correct· 2019-11-21
68.17
best: 75.14 (LiDAM)
SOTA
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Image ClassificationonSVHN, 250 Labels
Accuracy· 2019-11-21
96.79
best: 98.04 (ShrinkMatch)
SOTA
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Semi-Supervised Image ClassificationonCIFAR-10, 4000 Labels
Percentage error· 2019-11-21
4.18
best: 3.96 (SimMatch)
SOTA
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Semi-Supervised Image ClassificationonCIFAR-100, 1000 Labels
Percentage correct· 2019-11-21
41.27
SOTA
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Semi-Supervised Image ClassificationonSTL-10
Accuracy· 2019-11-21
95.48
SOTA
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Semi-Supervised Image ClassificationonSVHN, 1000 labels
Accuracy· 2019-11-21
97.58
SOTA
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Semi-Supervised Image ClassificationonCIFAR-100, 5000Labels
Percentage correct· 2019-11-21
68.17
best: 75.14 (LiDAM)
SOTA
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Semi-Supervised Image ClassificationonSVHN, 250 Labels
Accuracy· 2019-11-21
96.79
best: 98.04 (ShrinkMatch)
SOTA
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Image ClassificationonCIFAR-10, 250 Labels
Percentage error· 2019-12-18
7.6
best: 3.47 (SemiOccam)
RealMix: Towards Realistic Semi-Supervised Deep Learning Algorithms arXiv:1912.08766
Semi-Supervised Image ClassificationonCIFAR-10, 250 Labels
Percentage error· 2019-12-18
7.6
best: 3.47 (SemiOccam)
RealMix: Towards Realistic Semi-Supervised Deep Learning Algorithms arXiv:1912.08766
Image ClassificationonCIFAR-10
Percentage correct· 2019-11-21
98.01
best: 99.5 (ViT-H/14)
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Image ClassificationonCIFAR-100
Percentage correct· uses extra data· 2019-11-21
83.13
best: 96.08 (EffNet-L2 (SAM))
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Image ClassificationonSVHN
Percentage error· 2019-11-21
2.22
best: 1 (E2E-M3)
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Image ClassificationonSTL-10, 1000 Labels
Accuracy· 2019-11-21
91.96
best: 94.53 (NP-Match)
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Image Classificationoncifar10, 250 Labels
Percentage correct· 2019-11-21
92.4
best: 93.73 (ReMixMatch)
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Semi-Supervised Image ClassificationonSTL-10, 1000 Labels
Accuracy· 2019-11-21
91.96
best: 94.53 (NP-Match)
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265
Semi-Supervised Image Classificationoncifar10, 250 Labels
Percentage correct· 2019-11-21
92.4
best: 93.73 (ReMixMatch)
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations arXiv:1911.09265