About
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
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Subtasks
Datasets
Greatest papers with code
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.
Ranked #4 on
Image Clustering
on Tiny-ImageNet
CONDITIONAL IMAGE GENERATION IMAGE CLUSTERING UNSUPERVISED REPRESENTATION LEARNING
Auto-Encoding Variational Bayes
• pytorch/botorch
• 
First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods.
Ranked #6 on
Image Clustering
on Imagenet-dog-15
Symmetric Nonnegative Matrix Factorization for Graph Clustering
Unlike NMF, however, SymNMF is based on a similarity measure between data points, and factorizes a symmetric matrix containing pairwise similarity values (not necessarily nonnegative).
SCAN: Learning to Classify Images without Labels
ECCV 2020
• wvangansbeke/Unsupervised-Classification
•
First, a self-supervised task from representation learning is employed to obtain semantically meaningful features.
Ranked #1 on
Image Clustering
on CIFAR-10
IMAGE CLUSTERING REPRESENTATION LEARNING SEMI-SUPERVISED IMAGE CLASSIFICATION UNSUPERVISED IMAGE CLASSIFICATION
Self-labelling via simultaneous clustering and representation learning
ICLR 2020
• yukimasano/self-label
•
Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks.
Ranked #2 on
Image Clustering
on ImageNet
IMAGE CLUSTERING REPRESENTATION LEARNING SELF-SUPERVISED IMAGE CLASSIFICATION SELF-SUPERVISED LEARNING
Joint Unsupervised Learning of Deep Representations and Image Clusters
CVPR 2016
• jwyang/joint-unsupervised-learning
• 
In this paper, we propose a recurrent framework for Joint Unsupervised LEarning (JULE) of deep representations and image clusters.
Ranked #1 on
Image Clustering
on Coil-20
FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery
CVPR 2019
• kkanshul/finegan
•
We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained object categories.
Ranked #1 on
Image Generation
on CUB 128 x 128
CONDITIONAL IMAGE GENERATION FINE-GRAINED VISUAL CATEGORIZATION IMAGE CLUSTERING
Unsupervised Deep Embedding for Clustering Analysis
Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms.
Ranked #3 on
Unsupervised Image Classification
on SVHN
(using extra training data)
Deep Subspace Clustering Networks
NeurIPS 2017
• panji1990/Deep-subspace-clustering-networks
• 
We present a novel deep neural network architecture for unsupervised subspace clustering.
Ranked #3 on
Image Clustering
on Extended Yale-B
Sparse Subspace Clustering: Algorithm, Theory, and Applications
• panji1990/Deep-subspace-clustering-networks
•
In this paper, we propose and study an algorithm, called Sparse Subspace Clustering (SSC), to cluster data points that lie in a union of low-dimensional subspaces.
Ranked #4 on
Motion Segmentation
on Hopkins155