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Semi-Supervised Learning with Ladder Networks

arXiv 1507.02672 ↗First posted 9 Jul 2015cs.NE cs.LG stat.MLcited by 246

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Abstract (arXiv)

We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by backpropagation, avoiding the need for layer-wise pre-training. Our work builds on the Ladder network proposed by Valpola (2015), which we extend by combining the model with supervision. We show that the resulting model reaches state-of-the-art performance in semi-supervised MNIST and CIFAR-10 classification, in addition to permutation-invariant MNIST classification with all labels.

Key facts. Cited by 246 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jul 2015.

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Citing papers per year: 2020: 1; 2021: 88; 2022: 54; 2023: 53; 2024: 34; 2025: 13; 2026: 3.

246 papers.

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  38. 2023From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Cloudscited by 18

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