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 implementationsMost implemented papers
Self-Supervised Learning by Estimating Twin Class Distributions
To solve this problem, we propose to maximize the mutual information between the input and the class predictions.
PixelGAN Autoencoders
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.
Deep Transformation-Invariant Clustering
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.
Unsupervised Image Classification for Deep Representation Learning
Extensive experiments on ImageNet dataset have been conducted to prove the effectiveness of our method.
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.
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.
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.
Unsupervised Visual Representation Learning by Online Constrained K-Means
Clustering is to assign each instance a pseudo label that will be used to learn representations in discrimination.
iBOT: Image BERT Pre-Training with Online Tokenizer
We present a self-supervised framework iBOT that can perform masked prediction with an online tokenizer.
DeepDPM: Deep Clustering With an Unknown Number of Clusters
Using a split/merge framework, a dynamic architecture that adapts to the changing K, and a novel loss, our proposed method outperforms existing nonparametric methods (both classical and deep ones).