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Accuracy % with # of used labels1001000ALL
AtlasRBF (Pitelis et al., 2014) Deep Generative (Kingma et al., 2014) Virtual Adversarial (Miyato et al., 2016) Ladder Net (Rasmus et al., 2015)91.9 (± 0.95) 96.67(± 0.14) 97.67 98.94(±0.37)96.32 (± 0.12) 97.60(± 0.02) 98.64 99.16 (±0.08)98.69 99.04 99.36 99.43 (± 0.02)
Baseline: MLP, Gaussian Noise Baseline: Self-Training MLP with Semantic Loss (our)78.46 (±1.94) 72.55 (±4.21) 98.38 (±0.51)94.26 (±0.31) 87.43 (±3.07) 98.78 (±0.17)99.34(±0.08) 99.34 (±0.08) 99.36 (±0.02)
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Ladder Net (Rasmus et al., 2015)81.46(±0.64)85.18 (±0.27)86.48 (± 0.15)90.46
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MLP with Semantic Loss (our)86.74 (±0.71)89.49 (±0.24)89.67 (±0.09)89.81
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Accuracy % with # of used labels4000ALL
CNNBaselineinLadderNetLadder Net (Rasmus et al., 2015)Baseline: CNN,Whitening, CroppingCNN with Semantic Loss (our)76.67 (± 0.61)79.60 (±0.47)90.73
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Test accuracy %CoherentIncoherentConstraint
3-layer MLP1.0175.782.72
With semantic loss (our)13.5972.4355.28
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Test accuracy %CoherentIncoherentConstraint
5-layer MLP5.6285.916.99
With semantic loss (our)28.5183.1469.89
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CNNinLadderNetCNN in this paper
Input 32×32 RGB image
Resizing to 36 × 36 with padding Cropping Back
Whitening Contrast Normalization
Gaussian Noise with std.of 0.3
3 ×3 conv. 96 BNLeakyReLU3×3 conv. 96 BN ReLU
3×3 conv. 96 BNLeakyReLU3 ×3 conv. 96 BN ReLU
3 ×3 conv. 96 BNLeakyReLU3×3 conv. 96 BN ReLU
2×2 max-pooling stride 2 BN
3×3 conv.192BNLeakyReLU3×3 conv.192 BN ReLU
3×3 conv.192 BNLeakyReLU3×3 conv. 192 BN ReLU
3×3 conv.192 BN LeakyReLU3×3 conv.192 BN ReLU
2×2 max-pooling stride 2 BN
3×3conv.192BNLeakyReLU3×3conv.192 BNReLU
1×1 conv.192 BN LeakyReLU3×3 conv. 192 BN ReLU
1×1conv.10 BNLeakyReLU1×1 conv. 10 BN ReLu
global meanpool BN
fully connected BN
10-way softmax
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