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Unsupervised Image Classification
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Models that learn to label each image (i.e. cluster the dataset into its ground truth classes) without seeing the ground truth labels.
<span style="color:grey; opacity: 0.6"> Image credit: ImageNet clustering results of SCAN: Learning to Classify Images without Labels (ECCV 2020) </span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Breaking the Reclustering Barrier in Centroid-based Deep Clustering
Let Go of Your Labels with Unsupervised Transfer
IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature Representation
The VampPrior Mixture Model
Improving Cross-domain Few-shot Classification with Multilayer Perceptron
Stable Cluster Discrimination for Deep Clustering
Contrastive Knowledge Amalgamation for Unsupervised Image Classification
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision
MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillation
Minimalistic Unsupervised Learning with the Sparse Manifold Transform
Capsule Network based Contrastive Learning of Unsupervised Visual Representations
Loss Function Entropy Regularization for Diverse Decision Boundaries
LatentGAN Autoencoder: Learning Disentangled Latent Distribution
DeepDPM: Deep Clustering With an Unknown Number of Clusters
Revisiting the Transferability of Supervised Pretraining: an MLP Perspective
iBOT: Image BERT Pre-Training with Online Tokenizer
Self-Supervised Learning by Estimating Twin Class Distributions
ScatSimCLR: self-supervised contrastive learning with pretext task regularization for small-scale datasets
Unsupervised Visual Representation Learning by Online Constrained K-Means
Self-Supervised Classification Network
Combining pretrained CNN feature extractors to enhance clustering of complex natural images
Improving Unsupervised Image Clustering With Robust Learning
A Pseudo-labelling Auto-Encoder for unsupervised image classification
Unsupervised part representation by Flow Capsules
Unsupervised Image Classification Through Time-Multiplexed Photonic Multi-Layer Spiking Convolutional Neural Network
Improving Self-Organizing Maps with Unsupervised Feature Extraction
Self-Supervised Learning for Large-Scale Unsupervised Image Clustering
Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination
MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings
Unsupervised Image Classification for Deep Representation Learning
Deep Transformation-Invariant Clustering
SCAN: Learning to Classify Images without Labels
Self-supervised classification of dynamic obstacles using the temporal information provided by videos
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
Inferencing Based on Unsupervised Learning of Disentangled Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.