mlsanityData to 14 Oct 2026

Infinite Variational Autoencoder for Semi-Supervised Learning

arXiv 1611.07800 ↗First posted 23 Nov 2016cs.LG stat.MLcited by 23

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Abstract (arXiv)

This paper presents an infinite variational autoencoder (VAE) whose capacity adapts to suit the input data. This is achieved using a mixture model where the mixing coefficients are modeled by a Dirichlet process, allowing us to integrate over the coefficients when performing inference. Critically, this then allows us to automatically vary the number of autoencoders in the mixture based on the data. Experiments show the flexibility of our method, particularly for semi-supervised learning, where only a small number of training samples are available.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Nov 2016.

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Citing papers per year: 2021: 6; 2022: 4; 2023: 5; 2024: 4; 2025: 1; 2026: 3.

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  1. 2021A Survey on Deep Semi-supervised Learningcited by 251
  2. 2022Deep Clustering: A Comprehensive Surveycited by 78
  3. 2022Semi-Supervised and Unsupervised Deep Visual Learning: A Surveycited by 52
  4. 2022Continual Variational Autoencoder Learning via Online Cooperative Memorizationcited by 12
  5. 2024Computer Vision for Primate Behavior Analysis in the Wildcited by 10
  6. 2023Instructor-inspired Machine Learning for Robust Molecular Property Predictioncited by 8
  7. 2024BruSLeAttack: A Query-Efficient Score-Based Black-Box Sparse Adversarial Attackcited by 8
  8. 2021Learning from Noisy Labels via Dynamic Loss Thresholdingcited by 6
  9. 2024Joint Diffusion models in Continual Learningcited by 6
  10. 2023Semi-supervised Object Detection: A Survey on Recent Research and Progresscited by 5
  11. 2023A Hybrid of Generative and Discriminative Models Based on the Gaussian-coupled Softmax Layercited by 4
  12. 2021Deep Diffusion Processes for Active Learning of Hyperspectral Imagescited by 3
11 more
  1. 2026A Semi-supervised Molecular Learning Framework for Activity Cliff Estimationcited by 3
  2. 2026Multimodal Deep Generative Model for Semi-Supervised Learning under Class Imbalancecited by 3
  3. 2021Regularized Sequential Latent Variable Models with Adversarial Neural Networkscited by 1
  4. 2023Latent Graphs for Semi-Supervised Learning on Biomedical Tabular Datacited by 1
  5. 2023A Metalearned Neural Circuit for Nonparametric Bayesian Inferencecited by 1
  6. 2021Dream to Explore: Adaptive Simulations for Autonomous Systemscited by 0
  7. 2021Guided Generative Models using Weak Supervision for Detecting Object Spatial Arrangement in Overhead Imagescited by 0
  8. 2022Self Semi Supervised Neural Architecture Search for Semantic Segmentationcited by 0
  9. 2024Branched Variational Autoencoder Classifierscited by 0
  10. 2025Semi-Supervised Learning under General Causal Modelscited by 0
  11. 2026Stochastic Neural Networks for Quantum Devicescited by 0