Unsupervised Image Classification
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Benchmarks
Most implemented
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Adversarial Autoencoders
Unsupervised Deep Embedding for Clustering Analysis
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
iBOT: Image BERT Pre-Training with Online Tokenizer
Papers
Unsupervised Image Classification with Adaptive Nearest Neighbor Selection and Cluster Ensembles
Unsupervised image classification, or image clustering, aims to group unlabeled images into semantically meaningful categories. Early methods integrated representation learning and clustering within an iterative framewor…
Unsupervised Image ClassificationRepresentation LearningImage ClusteringBreaking the Reclustering Barrier in Centroid-based Deep Clustering
This work investigates an important phenomenon in centroid-based deep clustering (DC) algorithms: Performance quickly saturates after a period of rapid early gains. Practitioners commonly address early saturation with pe…
ClusteringDeep ClusteringImage ClusteringUnsupervised Image ClassificationLet Go of Your Labels with Unsupervised Transfer
Foundation vision-language models have enabled remarkable zero-shot transferability of the pre-trained representations to a wide range of downstream tasks. However, to solve a new task, zero-shot transfer still necessita…
Image ClusteringUnsupervised Image ClassificationIPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature Representation
Self-supervised learning with a contrastive batch approach has become a powerful tool for representation learning in computer vision. The performance of downstream tasks is proportional to the quality of visual features …
Contrastive LearningData Augmentationimage-classificationImage Classification+5The VampPrior Mixture Model
Current clustering priors for deep latent variable models (DLVMs) require defining the number of clusters a-priori and are susceptible to poor initializations. Addressing these deficiencies could greatly benefit deep lea…
ClusteringImage ClusteringmodelUnsupervised Image Classification+1Improving Cross-domain Few-shot Classification with Multilayer Perceptron
Cross-domain few-shot classification (CDFSC) is a challenging and tough task due to the significant distribution discrepancies across different domains. To address this challenge, many approaches aim to learn transferabl…
ClassificationCross-Domain Few-Shotimage-classificationImage Classification+1