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Machine learningDeep learning / NLP / CV

Semi-supervised Convolutional Neural Network

A Semi-supervised CNN trains a convolutional network on a small labeled image set and a larger pool of unlabeled images simultaneously, using techniques such as pseudo-labeling and consistency regularization to extract supervisory signal from unlabeled data. This strategy closes much of the performance gap caused by scarce annotations without requiring additional human labeling effort.

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Sources

  1. Lee, D.-H. (2013). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. ICML Workshop on Challenges in Representation Learning. link ↗
  2. Tarvainen, A. & Valpola, H. (2017). Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in Neural Information Processing Systems (NeurIPS), 30. link ↗

Related methods

Fine-Tuned Convolutional Neural NetworkSelf-supervised convolutional neural networkSemi-supervised Image ClassificationTransfer Learning with Convolutional Neural NetworkWeakly supervised convolutional neural network

Referenced by

Self-supervised convolutional neural networkSemi-supervised Instance SegmentationSemi-supervised Multilayer PerceptronSemi-supervised Object DetectionSemi-supervised Semantic SegmentationSemi-supervised TransformerSemi-supervised Variational AutoencoderSemi-supervised Vision TransformerWeakly supervised convolutional neural network

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ScholarGate — Semi-supervised Convolutional Neural Network (Semi-supervised Convolutional Neural Network (SSL-CNN)). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/semi-supervised-convolutional-neural-network