About

Accuracy on MNIST when training without any labels

Benchmarks

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

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.

DATA VISUALIZATION DIMENSIONALITY REDUCTION UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST VARIATIONAL INFERENCE

Invariant Information Clustering for Unsupervised Image Classification and Segmentation

ICCV 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.

IMAGE CLUSTERING SEMANTIC SEGMENTATION UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST

Stacked Capsule Autoencoders

NeurIPS 2019 phanideepgampa/stacked-capsule-networks

In the second stage, SCAE predicts parameters of a few object capsules, which are then used to reconstruct part poses.

CROSS-MODAL RETRIEVAL 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.

ROBUST CLASSIFICATION 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

Improving Self-Organizing Maps with Unsupervised Feature Extraction

4 Sep 2020lyes-khacef/GPU-SOM

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 gradient-based learning, and a neuroscience approach with Spiking Neural Networks using Spike Timing Dependant Plasticity learning.

UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST

PixelGAN Autoencoders

NeurIPS 2017 anonyme20/nips20

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, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code.

UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST