Unsupervised MNIST

5 papers with code · Methodology

Accuracy on MNIST when training without any labels

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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.

IMAGE GENERATION REPRESENTATION LEARNING UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST

Adversarial Autoencoders

18 Nov 2015eriklindernoren/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.

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.

SEMANTIC SEGMENTATION UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST

Inferencing Based on Unsupervised Learning of Disentangled Representations

7 Mar 2018tohinz/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.

UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST

Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

19 Nov 2015ZhimingZhou/AM-GAN

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.

UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST