Papers Unsupervised Image Classification
“Unsupervised Image Classification” 태그가 달린 논문 46편 · 필터 해제
Unsupervised Visual Representation Learning by Online Constrained K-Means
Cluster discrimination is an effective pretext task for unsupervised representation learning, which often consists of two phases: clustering and discrimination. Clustering is to assign each instance a pseudo label that w…
ClusteringContrastive LearningImage ClusteringMetric Learning+5Self-Supervised Classification Network
We present Self-Classifier -- a novel self-supervised end-to-end classification learning approach. Self-Classifier learns labels and representations simultaneously in a single-stage end-to-end manner by optimizing for sa…
ClassificationClusteringContrastive LearningGeneral Classification+3Combining pretrained CNN feature extractors to enhance clustering of complex natural images
Recently, a common starting point for solving complex unsupervised image classification tasks is to use generic features, extracted with deep Convolutional Neural Networks (CNN) pretrained on a large and versatile datase…
Clusteringimage-classificationImage ClassificationImage Clustering+1AC-VAE: Learning Semantic Representation with VAE for Adaptive Clustering
Unsupervised representation learning is essential in the field of machine learning, and accurate neighbor clusters of representation show great potential to support unsupervised image classification. This paper proposes …
ClassificationClusteringGeneral Classificationimage-classification+3Improving Unsupervised Image Clustering With Robust Learning
Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results. To overcome these challenges, the current resear…
ClusteringImage ClusteringUnsupervised Image ClassificationA Pseudo-labelling Auto-Encoder for unsupervised image classification
In this paper, we introduce a unique variant of the denoising Auto-Encoder and combine it with the perceptual loss to classify images in an unsupervised manner. The proposed method, called Pseudo Labelling, consists of f…
ClassificationData AugmentationDenoisingimage-classification+4Unsupervised part representation by Flow Capsules
Capsule networks aim to parse images into a hierarchy of objects, parts and relations. While promising, they remain limited by an inability to learn effective low level part descriptions. To address this issue we propose…
Decoderimage-classificationImage ClassificationUnsupervised Image ClassificationUnsupervised Image Classification Through Time-Multiplexed Photonic Multi-Layer Spiking Convolutional Neural Network
We present results of a deep photonic spiking convolutional neural network, based on two-section VCSELs, targeting image classification. Training is based on unsupervised spike-timing dependent plasticity, whereas neuron…
image-classificationImage ClassificationUnsupervised Image ClassificationImproving Self-Organizing Maps with Unsupervised Feature Extraction
The Self-Organizing Map (SOM) is a brain-inspired neural model that is very promising for unsupervised learning, especially in embedded applications. However, it is unable to learn efficient prototypes when dealing with …
ClassificationGeneral Classificationimage-classificationImage Classification+2Self-Supervised Learning for Large-Scale Unsupervised Image Clustering
Unsupervised learning has always been appealing to machine learning researchers and practitioners, allowing them to avoid an expensive and complicated process of labeling the data. However, unsupervised learning of compl…
ClusteringGeneral ClassificationImage ClassificationImage Clustering+3Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination
Unsupervised feature learning has made great strides with contrastive learning based on instance discrimination and invariant mapping, as benchmarked on curated class-balanced datasets. However, natural data could be hig…
Contrastive LearningSemi-Supervised Image ClassificationTransfer LearningUnsupervised Image ClassificationMitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification
Unsupervised image classification is a challenging computer vision task. Deep learning-based algorithms have achieved superb results, where the latest approach adopts unified losses from embedding and class assignment pr…
ClassificationGeneral Classificationimage-classificationImage Classification+2MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings
We present MIX'EM, a novel solution for unsupervised image classification. MIX'EM generates representations that by themselves are sufficient to drive a general-purpose clustering algorithm to deliver high-quality classi…
ClassificationClusteringGeneral Classificationimage-classification+3Unsupervised Image Classification for Deep Representation Learning
Deep clustering against self-supervised learning is a very important and promising direction for unsupervised visual representation learning since it requires little domain knowledge to design pretext tasks. However, the…
ClassificationClusteringContrastive LearningDeep Clustering+13Deep Transformation-Invariant Clustering
Recent advances in image clustering typically focus on learning better deep representations. In contrast, we present an orthogonal approach that does not rely on abstract features but instead learns to predict image tran…
ClusteringImage ClusteringUnsupervised Image ClassificationSCAN: Learning to Classify Images without Labels
Can we automatically group images into semantically meaningful clusters when ground-truth annotations are absent? The task of unsupervised image classification remains an important, and open challenge in computer vision.…
ClassificationClusteringGeneral Classificationimage-classification+5Self-supervised classification of dynamic obstacles using the temporal information provided by videos
Nowadays, autonomous driving systems can detect, segment, and classify the surrounding obstacles using a monocular camera. However, state-of-the-art methods solving these tasks generally perform a fully supervised learni…
Autonomous DrivingGeneral Classificationimage-classificationImage Classification+2Invariant Information Clustering for Unsupervised Image Classification and Segmentation
We present a novel clustering objective that learns a neural network classifier from scratch, given only unlabelled data samples. The model discovers clusters that accurately match semantic classes, achieving state-of-th…
ClusteringGeneral Classificationimage-classificationImage Classification+5Inferencing Based on Unsupervised Learning of Disentangled Representations
Combining Generative Adversarial Networks (GANs) with encoders that learn to encode data points has shown promising results in learning data representations in an unsupervised way. We propose a framework that combines an…
DescriptiveRepresentation LearningUnsupervised Image ClassificationUnsupervised MNISTLearning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization
In this paper, we propose a novel unsupervised clustering approach exploiting the hidden information that is indirectly introduced through a pseudo classification objective. Specifically, we randomly assign a pseudo pare…
ClusteringUnsupervised Image Classification