Unsupervised MNIST
Accuracy on MNIST when training without any labels
State-of-the-art leaderboards
| Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
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Greatest papers with code
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
NeurIPS 2016
• tensorflow/models
• 
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.
#5 best model for
Unsupervised MNIST
on MNIST
IMAGE GENERATION REPRESENTATION LEARNING UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST
Adversarial Autoencoders
• eriklindernoren/PyTorch-GAN
• 
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 aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution.
#3 best model for
Unsupervised MNIST
on MNIST
DIMENSIONALITY REDUCTION UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
arXiv 2019
• xu-ji/IIC
•
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.
SOTA for Unsupervised MNIST on MNIST
SEMANTIC SEGMENTATION UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST
Inferencing Based on Unsupervised Learning of Disentangled Representations
• tohinz/Bidirectional-InfoGAN
•
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.
#2 best model for
Unsupervised MNIST
on MNIST
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
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 generative model.
#4 best model for
Unsupervised MNIST
on MNIST