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ResearchFormerly WizWand

Phase classification on Pouring (test)

91.82AccuracyTCC
40.766454.020767.27580.5293Apr 16, 2019
Updated 8mo ago

Evaluation results

MethodLinks
TCCBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=1.02019.0491.82
Supervised LearningBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=1.02019.0491.55
TCC + TCNBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=1.02019.0491.51
TCCBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=0.52019.0491.43
TCC + TCNBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=0.52019.0491.23
TCC + SaLBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=1.02019.0490.75
TCC + SaLBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=0.52019.0490.69
TCNBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=0.52019.0490.39
TCNBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=1.02019.0490.35
TCC% of Labels=1.0, Backbone=VGG-M, Training Protocol=from scratch2019.0490.21
TCC% of Labels=0.5, Backbone=VGG-M, Training Protocol=from scratch2019.0489.43
TCCBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=0.12019.0489.23
TCC + SaLBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=0.12019.0489.21
TCNBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=0.12019.0489.19
TCC + TCNBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=0.12019.0489.17
Supervised Learning% of Labels=1.0, Backbone=VGG-M, Training Protocol=from scratch2019.0488.41
SaLBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=1.02019.0488.02
SaLBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=0.52019.0487.84
TCC% of Labels=0.1, Backbone=VGG-M, Training Protocol=from scratch2019.0486.82
Supervised LearningBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=0.52019.0486.14
SaLBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=0.12019.0485.68
TCN% of Labels=1.0, Backbone=VGG-M, Training Protocol=from scratch2019.0484.57
TCN% of Labels=0.5, Backbone=VGG-M, Training Protocol=from scratch2019.0483.27
SaL% of Labels=1.0, Backbone=VGG-M, Training Protocol=from scratch2019.0483.19
SaL% of Labels=0.5, Backbone=VGG-M, Training Protocol=from scratch2019.0480.96
Supervised Learning% of Labels=0.5, Backbone=VGG-M, Training Protocol=from scratch2019.0477.67
TCN% of Labels=0.1, Backbone=VGG-M, Training Protocol=from scratch2019.0476.03
Supervised LearningBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=0.12019.0475.43
SaL% of Labels=0.1, Backbone=VGG-M, Training Protocol=from scratch2019.0474.5
Supervised Learning% of Labels=0.1, Backbone=VGG-M, Training Protocol=from scratch2019.0462.01
ImageNet FeaturesBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=1.02019.0451.13
Random FeaturesBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=1.02019.0446.08
ImageNet FeaturesBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=0.52019.0446.06
Random FeaturesBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=0.52019.0445.94
ImageNet FeaturesBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=0.12019.0443.85
Random FeaturesBackbone=ResNet-50, Pre-trained=ImageNet, Evaluation Protocol=Fine-tuning, % of Labels=0.12019.0442.73