Unsupervised MNIST

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

9 shown of 9 papers with code (10 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
  12 Jun 2016 38 repositories listed Syntology ran 5 of 6 samples · 1 unverified
  This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner.
• Adversarial Autoencoders
  18 Nov 2015 29 repositories listed Syntology ran 8 of 12 samples · 4 unverified · 9 pointer-only (licence)
  In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the…
• Stacked Capsule Autoencoders
  17 Jun 2019 11 repositories listed Syntology ran 2 of 29 samples · 27 unverified
  In the second stage, SCAE predicts parameters of a few object capsules, which are then used to reconstruct part poses.
• Invariant Information Clustering for Unsupervised Image Classification and Segmentation
  17 Jul 2018 6 repositories listed Syntology ran 2 of 18 samples · 16 unverified
  The method is not specialised to computer vision and operates on any paired dataset samples; in our experiments we use random transforms to obtain a pair from each image.
• Ladder Variational Autoencoders
  6 Feb 2016 5 repositories listed Syntology ran 2 of 9 samples · 7 unverified
  Variational Autoencoders are powerful models for unsupervised learning.
• Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
  19 Nov 2015 5 repositories listed
  Our approach is based on an objective function that trades-off mutual information between observed examples and their predicted categorical class distribution, against robustness of the classifier to an adversarial…
• Inferencing Based on Unsupervised Learning of Disentangled Representations
  7 Mar 2018 2 repositories listed
  Combining Generative Adversarial Networks (GANs) with encoders that learn to encode data points has shown promising results in learning data representations in an unsupervised way.
• Improving Self-Organizing Maps with Unsupervised Feature Extraction
  4 Sep 2020 1 repository listed
  We conduct a comparative study on the SOM classification accuracy with unsupervised feature extraction using two different approaches: a machine learning approach with Sparse Convolutional Auto-Encoders using…
• PixelGAN Autoencoders
  2 Jun 2017 1 repository listed
  In this paper, we describe the "PixelGAN autoencoder", a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels (PixelCNN) that is conditioned on a latent code,…

Syntology lines on 5 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.