Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks
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Summary
Without any unsupervised pre-training method, this simple method with dropout shows the state-of-the-art performance of semi-supervised learning for deep neural networks.
- Published
- 2013-01-01
- Cited by
- 4,788
- References
- 17
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:18507866
References
- Semi-Supervised Classification by Low Density Separation
- Improving neural networks by preventing co-adaptation of feature detectors
- Classification using discriminative restricted Boltzmann machines
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- Semi-supervised learning of compact document representations with deep networks
- Representation Learning: A Review and New Perspectives
- The Manifold Tangent Classifier
- Visualizing Data using t-SNE
- Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
- Why Does Unsupervised Pre-training Help Deep Learning?
- Bagging Predictors
- Semi-Supervised Learning
- This PDF file includes: Materials and Methods
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- Learning a Deep Hybrid Model for Semi-Supervised Text Classification
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- Online Semi-Supervised Learning with Deep Hybrid Boltzmann Machines and Denoising Autoencoders
- Semi-supervised Learning for Convolutional Neural Networks via Online Graph Construction
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- Pseudo-Supervised Training Improves Unsupervised Melody Segmentation
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- SSDH: Semi-Supervised Deep Hashing for Large Scale Image Retrieval
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