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
The VampPrior Mixture ModelCode0
IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature RepresentationCode0
MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillationCode0
Learning Discrete Representations via Information Maximizing Self-Augmented TrainingCode0
PixelGAN AutoencodersCode0
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
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision—0
Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization—0
Combining pretrained CNN feature extractors to enhance clustering of complex natural images—0
Loss Function Entropy Regularization for Diverse Decision Boundaries—0
Minimalistic Unsupervised Learning with the Sparse Manifold Transform—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
GUIDED MCMC FOR SPARSE BAYESIAN MODELS TO DETECT RARE EVENTS IN IMAGES SANS LABELED DATA—0
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