EnAET: Self-Trained Ensemble AutoEncoding Transformations for Semi-Supervised Learning

21 Nov 2019Xiao WangDaisuke KiharaJiebo LuoGuo-Jun Qi

Deep neural networks have been successfully applied to many real-world applications. However, these successes rely heavily on large amounts of labeled data, which is expensive to obtain... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Image Classification CIFAR-10 EnAET Percentage correct 98.01 # 9
Percentage error 1.99 # 5
Image Classification CIFAR-100 EnAET Percentage correct 83.13 # 18
Semi-Supervised Image Classification cifar-100, 10000 Labels EnAET Accuracy 77.08 # 1
Semi-Supervised Image Classification CIFAR-100, 1000 Labels EnAET Percentage correct 41.27 # 1
Semi-Supervised Image Classification CIFAR-100, 5000Labels EnAET Percentage correct 68.17 # 1
Semi-Supervised Image Classification cifar10, 250 Labels EnAET Percentage correct 92.4 # 2
Semi-Supervised Image Classification CIFAR-10, 4000 Labels EnAET Accuracy 95.82 # 1
Semi-Supervised Image Classification STL-10 EnAET Accuracy 95.48 # 1
Image Classification STL-10 EnAET Percentage correct 95.48 # 5
Semi-Supervised Image Classification STL-10, 1000 Labels EnAET Accuracy 91.96 # 3
Image Classification SVHN EnAET Percentage error 2.22 # 19
Semi-Supervised Image Classification SVHN, 1000 labels EnAET Accuracy 97.58 # 2
Semi-Supervised Image Classification SVHN, 250 Labels EnAET Accuracy 96.79 # 1

Methods used in the Paper


METHOD TYPE
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