Unsupervised Image Classification
23 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)
Benchmarks
These leaderboards are used to track progress in Unsupervised Image Classification
Libraries
Use these libraries to find Unsupervised Image Classification models and implementations
CIFAR-10
ImageNet
MNIST
SVHN
STL-10
ObjectNet
Most implemented papers
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
eriklindernoren/PyTorch-GAN
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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
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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
piiswrong/dec
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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
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
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
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
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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
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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 Classification Network
elad-amrani/self-classifier
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To guarantee non-degenerate solutions (i. e., solutions where all labels are assigned to the same class) we propose a mathematically motivated variant of the cross-entropy loss that has a uniform prior asserted on the predicted labels.
Self-Supervised Learning by Estimating Twin Class Distributions
bytedance/TWIST
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To solve this problem, we propose to maximize the mutual information between the input and the class predictions.
