Image Classification on MIT67
81.1AccuracyRényiCL
Evaluation Results
| Method | Links | |
|---|---|---|
| RényiCLEvaluation Protocol=linear evaluation, Multi-crop Augmentation=true2022.08 | 81.1 | |
| CLSAEvaluation Protocol=linear evaluation, Multi-crop Augmentation=true2022.08 | 77.7 | |
| CLSAEvaluation Protocol=linear evaluation, Multi-crop Augmentation=false2022.08 | 77.1 | |
| InfoMinEvaluation Protocol=linear evaluation, Multi-crop Augmentation=false2022.08 | 76.9 | |
| RényiCLEvaluation Protocol=linear evaluation, Multi-crop Augmentation=false2022.08 | 75.8 | |
| AFDTeacher backbone=ResNet34, Student backbone=ResNet18, Pre-trained dataset=ImageNet, Evaluation protocol=fine-tuning2021.02 | 75.07 | |
| L2TTeacher backbone=ResNet34, Student backbone=ResNet18, Pre-trained dataset=ImageNet, Evaluation protocol=fine-tuning2021.02 | 74.85 | |
| ATTTeacher backbone=ResNet34, Student backbone=ResNet18, Pre-trained dataset=ImageNet, Evaluation protocol=fine-tuning2021.02 | 72.54 | |
| FitNetTeacher backbone=ResNet34, Student backbone=ResNet18, Pre-trained dataset=ImageNet, Evaluation protocol=fine-tuning2021.02 | 71.72 | |
| Fine-tuningTeacher backbone=ResNet34, Student backbone=ResNet18, Pre-trained dataset=ImageNet, Evaluation protocol=fine-tuning2021.02 | 70.67 | |
| SimSiam + AugSelfFine-tuning strategy=L2SP, Pre-trained on=ImageNet100, Backbone=ResNet-502021.11 | 69.31 | |
| SimSiam + AugSelfBackbone=ResNet-50, Pre-training Dataset=ImageNet100, Evaluation Protocol=Linear evaluation2021.11 | 67.76 | |
| SimSiamFine-tuning strategy=L2SP, Pre-trained on=ImageNet100, Backbone=ResNet-502021.11 | 67.69 | |
| AFDTeacher architecture=ResNet34, Student architecture=ResNet18, Source domain=ImageNet, Number of experiment repeats=3, Beta=1,0002021.02 | 66.47 | |
| SimSiamBackbone=ResNet-50, Pre-training Dataset=ImageNet100, Evaluation Protocol=Linear evaluation2021.11 | 65.75 | |
| L2TTeacher architecture=ResNet34, Student architecture=ResNet18, Source domain=ImageNet, Number of experiment repeats=32021.02 | 64.85 | |
| MoCo v2 + AugSelfBackbone=ResNet-50, Pre-training Dataset=ImageNet100, Evaluation Protocol=Linear evaluation2021.11 | 63.36 | |
| MoCo v2Backbone=ResNet-50, Pre-training Dataset=ImageNet100, Evaluation Protocol=Linear evaluation2021.11 | 61.64 | |
| ATTTeacher architecture=ResNet34, Student architecture=ResNet18, Source domain=ImageNet, Number of experiment repeats=32021.02 | 59.18 | |
| FitNetTeacher architecture=ResNet34, Student architecture=ResNet18, Source domain=ImageNet, Number of experiment repeats=32021.02 | 54.88 | |
| Supervised + AugSelfBackbone=ResNet-50, Pre-training Dataset=ImageNet100, Evaluation Protocol=Linear evaluation2021.11 | 54.63 | |
| SupervisedBackbone=ResNet-50, Pre-training Dataset=ImageNet100, Evaluation Protocol=Linear evaluation2021.11 | 52.91 | |
| ScratchTeacher architecture=ResNet34, Student architecture=ResNet18, Source domain=ImageNet, Number of experiment repeats=32021.02 | 48.91 | |
| BYOLAugSelf=true2021.11 | 46.17 | |
| SimSiam + AugSelfBackbone=ResNet-18, Pre-training Dataset=STL10, Evaluation Protocol=Linear evaluation2021.11 | 45.67 | |
| BYOLAugSelf=false2021.11 | 42.78 | |
| MoCo v2 + AugSelfBackbone=ResNet-18, Pre-training Dataset=STL10, Evaluation Protocol=Linear evaluation2021.11 | 41.67 | |
| SimCLRAugSelf=true2021.11 | 40.62 | |
| SimSiamBackbone=ResNet-18, Pre-training Dataset=STL10, Evaluation Protocol=Linear evaluation2021.11 | 39.15 | |
| SwAVAugSelf=true2021.11 | 39.13 | |
| MoCo v2Backbone=ResNet-18, Pre-training Dataset=STL10, Evaluation Protocol=Linear evaluation2021.11 | 39.01 | |
| SimCLRAugSelf=false2021.11 | 36.82 | |
| SwAVAugSelf=false2021.11 | 36.69 |