TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Papers/Semi-Supervised Learning with Ladder Networks

Semi-Supervised Learning with Ladder Networks

Antti Rasmus, Harri Valpola, Mikko Honkala, Mathias Berglund, Tapani Raiko

2015-07-09NeurIPS 2015 12General ClassificationSemi-Supervised Image Classification
PaperPDFCodeCodeCodeCodeCode(official)CodeCodeCodeCodeCode

Abstract

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.

Results

TaskDatasetMetricValueModel
Image ClassificationCIFAR-10, 4000 LabelsPercentage error20.4Γ-model
Semi-Supervised Image ClassificationCIFAR-10, 4000 LabelsPercentage error20.4Γ-model

Related Papers

ViTSGMM: A Robust Semi-Supervised Image Recognition Network Using Sparse Labels2025-06-04Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning2025-05-26Simple Semi-supervised Knowledge Distillation from Vision-Language Models via $\mathbf{\texttt{D}}$ual-$\mathbf{\texttt{H}}$ead $\mathbf{\texttt{O}}$ptimization2025-05-12Weakly Semi-supervised Whole Slide Image Classification by Two-level Cross Consistency Supervision2025-04-16Specialized text classification: an approach to classifying Open Banking transactions2025-04-10Diff-SySC: An Approach Using Diffusion Models for Semi-Supervised Image Classification2025-02-25Universal Training of Neural Networks to Achieve Bayes Optimal Classification Accuracy2025-01-13Revisiting MLLMs: An In-Depth Analysis of Image Classification Abilities2024-12-21