Papers Unsupervised Image Classification
“Unsupervised Image Classification” 태그가 달린 논문 46편 · 필터 해제
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+1Stable Cluster Discrimination for Deep Clustering
Deep clustering can optimize representations of instances (i.e., representation learning) and explore the inherent data distribution (i.e., clustering) simultaneously, which demonstrates a superior performance over conve…
ClusteringDeep ClusteringImage ClusteringRepresentation Learning+1The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning
We present HUME, a simple model-agnostic framework for inferring human labeling of a given dataset without any external supervision. The key insight behind our approach is that classes defined by many human labelings are…
Image ClusteringUnsupervised Image ClassificationContrastive Knowledge Amalgamation for Unsupervised Image Classification
Knowledge amalgamation (KA) aims to learn a compact student model to handle the joint objective from multiple teacher models that are are specialized for their own tasks respectively. Current methods focus on coarsely al…
Classificationimage-classificationImage ClassificationUnsupervised Image ClassificationContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision
The recent advances in representation learning inspire us to take on the challenging problem of unsupervised image classification tasks in a principled way. We propose ContraCluster, an unsupervised image classification …
Contrastive Learningimage-classificationImage ClassificationRepresentation Learning+2MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillation
We present a new method of self-supervised learning and knowledge distillation based on the multi-views and multi-representations (MV-MR). The MV-MR is based on the maximization of dependence between learnable embeddings…
ClusteringContrastive LearningKnowledge DistillationLinear evaluation+3Minimalistic Unsupervised Learning with the Sparse Manifold Transform
We describe a minimalistic and interpretable method for unsupervised learning, without resorting to data augmentation, hyperparameter tuning, or other engineering designs, that achieves performance close to the SOTA SSL …
Self-Supervised LearningSparse Representation-based ClassificationSpectral Graph ClusteringUnsupervised Image Classification+1Capsule Network based Contrastive Learning of Unsupervised Visual Representations
Capsule Networks have shown tremendous advancement in the past decade, outperforming the traditional CNNs in various task due to it's equivariant properties. With the use of vector I/O which provides information of both …
Contrastive Learningimage-classificationImage ClassificationUnsupervised Image ClassificationLoss Function Entropy Regularization for Diverse Decision Boundaries
Is it possible to train several classifiers to perform meaningful crowd-sourcing to produce a better prediction label set without ground-truth annotation? This paper will modify the contrastive learning objectives to aut…
Contrastive LearningDenoisingPredictionUnsupervised Image ClassificationLatentGAN Autoencoder: Learning Disentangled Latent Distribution
In autoencoder, the encoder generally approximates the latent distribution over the dataset, and the decoder generates samples using this learned latent distribution. There is very little control over the latent vector a…
Decoderimage-classificationImage ClassificationUnsupervised Image ClassificationDeepDPM: Deep Clustering With an Unknown Number of Clusters
Deep Learning (DL) has shown great promise in the unsupervised task of clustering. That said, while in classical (i.e., non-deep) clustering the benefits of the nonparametric approach are well known, most deep-clustering…
ClusteringDeep ClusteringDeep Nonparametric ClusteringModel Selection+2Revisiting the Transferability of Supervised Pretraining: an MLP Perspective
The pretrain-finetune paradigm is a classical pipeline in visual learning. Recent progress on unsupervised pretraining methods shows superior transfer performance to their supervised counterparts. This paper revisits thi…
domain classificationimage-classificationImage ClassificationLinear evaluation+3iBOT: Image BERT Pre-Training with Online Tokenizer
The success of language Transformers is primarily attributed to the pretext task of masked language modeling (MLM), where texts are first tokenized into semantically meaningful pieces. In this work, we study masked image…
image-classificationImage ClassificationInstance SegmentationLanguage Modeling+7Self-Supervised Learning by Estimating Twin Class Distributions
We present TWIST, a simple and theoretically explainable self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way. We employ a siamese network terminated by a soft…
Fine-Grained Image ClassificationImage ClassificationRepresentation LearningSelf-Supervised Image Classification+4GUIDED MCMC FOR SPARSE BAYESIAN MODELS TO DETECT RARE EVENTS IN IMAGES SANS LABELED DATA
Detection of rare events in images is a challenging task because of two main problems, the first problem is the lack of labeled data for rare category class and the second problem is a highly imbalanced data problem. Tra…
image-classificationImage ClassificationUnsupervised Image ClassificationZero-Shot Learning