Unsupervised Image Classification
28 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
Latest papers
Unsupervised Visual Representation Learning by Online Constrained K-Means
idstcv/coke
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CVPR 2022
Clustering is to assign each instance a pseudo label that will be used to learn representations in discrimination.
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.
Improving Unsupervised Image Clustering With Robust Learning
Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results.
Improving Self-Organizing Maps with Unsupervised Feature Extraction
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.
Self-Supervised Learning for Large-Scale Unsupervised Image Clustering
Unsupervised learning has always been appealing to machine learning researchers and practitioners, allowing them to avoid an expensive and complicated process of labeling the data.
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.
Unsupervised Image Classification for Deep Representation Learning
HIK-LAB/Unsupervised-Image-Classification
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Extensive experiments on ImageNet dataset have been conducted to prove the effectiveness of our method.
Deep Transformation-Invariant Clustering
monniert/dti-clustering
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NeurIPS 2020
In contrast, we present an orthogonal approach that does not rely on abstract features but instead learns to predict image transformations and performs clustering directly in image space.
SCAN: Learning to Classify Images without Labels
wvangansbeke/Unsupervised-Classification
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ECCV 2020
First, a self-supervised task from representation learning is employed to obtain semantically meaningful features.
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.
