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
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision—0
MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillationCode0
Minimalistic Unsupervised Learning with the Sparse Manifold Transform—0
Loss Function Entropy Regularization for Diverse Decision Boundaries—0
LatentGAN Autoencoder: Learning Disentangled Latent Distribution—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
Combining pretrained CNN feature extractors to enhance clustering of complex natural images—0
AC-VAE: Learning Semantic Representation with VAE for Adaptive Clustering—0
A Pseudo-labelling Auto-Encoder for unsupervised image classification—0
Unsupervised part representation by Flow Capsules—0
Unsupervised Image Classification Through Time-Multiplexed Photonic Multi-Layer Spiking Convolutional Neural Network—0
MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings—0
Unsupervised Image Classification for Deep Representation LearningCode0
Self-supervised classification of dynamic obstacles using the temporal information provided by videos—0
Inferencing Based on Unsupervised Learning of Disentangled RepresentationsCode0
Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization—0
PixelGAN AutoencodersCode0
Learning Discrete Representations via Information Maximizing Self-Augmented TrainingCode0
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial NetworksCode0
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