Unsupervised Image Classification

21 papers with code • 7 benchmarks • 6 datasets

Models that learn to label each image (i.e. cluster the dataset into its ground truth classes) without seeing the ground truth labels.

Image credit: ImageNet clustering results of SCAN: Learning to Classify Images without Labels (ECCV 2020)


Use these libraries to find Unsupervised Image Classification models and implementations

Most implemented papers

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

eriklindernoren/PyTorch-GAN NeurIPS 2016

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

eriklindernoren/PyTorch-GAN 18 Nov 2015

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.

Unsupervised Deep Embedding for Clustering Analysis

elieJalbout/Clustering-with-Deep-learning 19 Nov 2015

Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms.

Invariant Information Clustering for Unsupervised Image Classification and Segmentation

xu-ji/IIC ICCV 2019

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.

Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

ZhimingZhou/AM-GAN 19 Nov 2015

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.

Learning Discrete Representations via Information Maximizing Self-Augmented Training

weihua916/imsat ICML 2017

Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation.

Inferencing Based on Unsupervised Learning of Disentangled Representations

tohinz/Bidirectional-InfoGAN 7 Mar 2018

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 Feature Learning by Cross-Level Instance-Group Discrimination

frank-xwang/CLD-UnsupervisedLearning CVPR 2021

Unsupervised feature learning has made great strides with contrastive learning based on instance discrimination and invariant mapping, as benchmarked on curated class-balanced datasets.

Self-Supervised Learning by Estimating Twin Class Distributions

bytedance/TWIST 14 Oct 2021

To solve this problem, we propose to maximize the mutual information between the input and the class predictions.

PixelGAN Autoencoders

anonyme20/nips20 NeurIPS 2017

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.