SOTAVerified

Unsupervised Image Classification

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)

Papers

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Showing 26–45 of 45 papers

TitleStatusHype
Minimalistic Unsupervised Learning with the Sparse Manifold Transform—0
GUIDED MCMC FOR SPARSE BAYESIAN MODELS TO DETECT RARE EVENTS IN IMAGES SANS LABELED DATA—0
A Pseudo-labelling Auto-Encoder for unsupervised image classification—0
Self-supervised classification of dynamic obstacles using the temporal information provided by videos—0
Unsupervised part representation by Flow Capsules—0
AC-VAE: Learning Semantic Representation with VAE for Adaptive Clustering—0
Unsupervised Image Classification Through Time-Multiplexed Photonic Multi-Layer Spiking Convolutional Neural Network—0
LatentGAN Autoencoder: Learning Disentangled Latent Distribution—0
Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization—0
MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings—0
Contrastive Knowledge Amalgamation for Unsupervised Image Classification—0
Revisiting the Transferability of Supervised Pretraining: an MLP Perspective—0
The VampPrior Mixture ModelCode0
Inferencing Based on Unsupervised Learning of Disentangled RepresentationsCode0
MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillationCode0
PixelGAN AutoencodersCode0
Unsupervised Image Classification for Deep Representation LearningCode0
Learning Discrete Representations via Information Maximizing Self-Augmented TrainingCode0
IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature RepresentationCode0
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial NetworksCode0
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