Papers Self-Supervised Image Classification
“Self-Supervised Image Classification” 태그가 달린 논문 110편 · 필터 해제
Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
Large-scale labeled data are generally required to train deep neural networks in order to obtain better performance in visual feature learning from images or videos for computer vision applications. To avoid extensive co…
Self-Supervised Image ClassificationSelf-Supervised LearningRevisiting Self-Supervised Visual Representation Learning
Unsupervised visual representation learning remains a largely unsolved problem in computer vision research. Among a big body of recently proposed approaches for unsupervised learning of visual representations, a class of…
Representation LearningSelf-Supervised Image ClassificationSelf-Supervised LearningDeep Clustering for Unsupervised Learning of Visual Features
Clustering is a class of unsupervised learning methods that has been extensively applied and studied in computer vision. Little work has been done to adapt it to the end-to-end training of visual features on large scale …
ClusteringDeep ClusteringImage ClusteringSelf-Supervised Image Classification+2Representation Learning with Contrastive Predictive Coding
While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In th…
Reinforcement LearningRepresentation LearningSelf-Supervised Image ClassificationSemi-Supervised Image ClassificationUnsupervised Feature Learning via Non-Parametric Instance Discrimination
Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether this observation can be extended beyond th…
General Classificationobject-detectionObject DetectionRetrieval+2Unsupervised Representation Learning by Predicting Image Rotations
Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features. However, in order to suc…
General ClassificationRepresentation LearningSelf-Supervised Image ClassificationMulti-task Self-Supervised Visual Learning
We investigate methods for combining multiple self-supervised tasks--i.e., supervised tasks where data can be collected without manual labeling--in order to train a single visual representation. First, we provide an appl…
Depth EstimationDepth PredictionGeneral ClassificationSelf-Supervised Image ClassificationRepresentation Learning by Learning to Count
We introduce a novel method for representation learning that uses an artificial supervision signal based on counting visual primitives. This supervision signal is obtained from an equivariance relation, which does not re…
RelationRepresentation LearningSelf-Supervised Image ClassificationTransfer LearningSplit-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
We propose split-brain autoencoders, a straightforward modification of the traditional autoencoder architecture, for unsupervised representation learning. The method adds a split to the network, resulting in two disjoint…
Representation LearningSelf-Supervised Image ClassificationTransfer LearningColorful Image Colorization
Given a grayscale photograph as input, this paper attacks the problem of hallucinating a plausible color version of the photograph. This problem is clearly underconstrained, so previous approaches have either relied on s…
ColorizationDiversityImage ColorizationSelf-Supervised Image Classification