Papers Semi-Supervised Image Classification
“Semi-Supervised Image Classification” 태그가 달린 논문 169편 · 필터 해제
Vanishing Twin GAN: How training a weak Generative Adversarial Network can improve semi-supervised image classification
Generative Adversarial Networks can learn the mapping of random noise to realistic images in a semi-supervised framework. This mapping ability can be used for semi-supervised image classification to detect images of an u…
ClassificationGeneral ClassificationGenerative Adversarial Networkimage-classification+2Self-supervised Pretraining of Visual Features in the Wild
Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods. These results have been achieved in a control environment, that is the highly curated ImageNet dat…
Image ClassificationSelf-Supervised Image ClassificationSelf-Supervised LearningSemi-Supervised Image ClassificationLearning Transferable Visual Models From Natural Language Supervision
State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is n…
Action RecognitionBenchmarkingFew-Shot Image Classificationgeo-localization+21Multi-class Generative Adversarial Nets for Semi-supervised Image Classification
From generating never-before-seen images to domain adaptation, applications of Generative Adversarial Networks (GANs) spread wide in the domain of vision and graphics problems. With the remarkable ability of GANs in lear…
ClassificationDomain AdaptationGeneral Classificationimage-classification+3Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning
We present a plug-in replacement for batch normalization (BN) called exponential moving average normalization (EMAN), which improves the performance of existing student-teacher based self- and semi-supervised learning te…
Self-Supervised LearningSemi-Supervised Image ClassificationSelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning
This paper introduces SelfMatch, a semi-supervised learning method that combines the power of contrastive self-supervised learning and consistency regularization. SelfMatch consists of two stages: (1) self-supervised pre…
Contrastive LearningSelf-Supervised LearningSemi-Supervised Image ClassificationIn Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance. However, they heavily rely on domain-specific data augmentations, wh…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPseudo LabelSemi-Supervised Image Classification+2One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features
We introduce a model that segments lesions and predicts COVID-19 from chest CT scans through the derivation of an affinity matrix between lesion masks. The novelty of the methodology is based on the computation of the …
COVID-19 DiagnosisInstance SegmentationSemi-Supervised Image ClassificationSensitivityEC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs
Semi-supervised learning has been gaining attention as it allows for performing image analysis tasks such as classification with limited labeled data. Some popular algorithms using Generative Adversarial Networks (GANs) …
ClassificationData AugmentationGeneral ClassificationImage Generation+2OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning
Learning image representations without human supervision is an important and active research field. Several recent approaches have successfully leveraged the idea of making such a representation invariant under different…
object-detectionObject DetectionRepresentation LearningSelf-Supervised Image Classification+3[Re] Reimplementation of FixMatch and Investigation on Noisy (Pseudo) Labels and Confirmation Errors of FixMatch
FixMatch is a semi-supervised learning method, which achieves comparable results with fully supervised learning by leveraging a limited number of labeled data (pseudo labelling technique) and taking a good use of the unl…
Semi-Supervised Image ClassificationBoosting the Performance of Semi-Supervised Learning with Unsupervised Clustering
Recently, Semi-Supervised Learning (SSL) has shown much promise in leveraging unlabeled data while being provided with very few labels. In this paper, we show that ignoring the labels altogether for whole epochs intermit…
ClusteringSemi-Supervised Image ClassificationCoMatch: Semi-supervised Learning with Contrastive Graph Regularization
Semi-supervised learning has been an effective paradigm for leveraging unlabeled data to reduce the reliance on labeled data. We propose CoMatch, a new semi-supervised learning method that unifies dominant approaches and…
Contrastive LearningRepresentation LearningSelf-Supervised LearningSemi-Supervised Image ClassificationKeepAugment: A Simple Information-Preserving Data Augmentation Approach
Data augmentation (DA) is an essential technique for training state-of-the-art deep learning systems. In this paper, we empirically show data augmentation might introduce noisy augmented examples and consequently hurt th…
Data AugmentationGeneral Classificationimage-classificationImage Classification+3Boosting Contrastive Self-Supervised Learning with False Negative Cancellation
Self-supervised representation learning has made significant leaps fueled by progress in contrastive learning, which seeks to learn transformations that embed positive input pairs nearby, while pushing negative pairs far…
Contrastive LearningRepresentation LearningSelf-Supervised Image ClassificationSelf-Supervised Learning+1SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
Semi-supervised variational autoencoders (VAEs) have obtained strong results, but have also encountered the challenge that good ELBO values do not always imply accurate inference results. In this paper, we investigate an…
4kSemi-Supervised Image ClassificationVariational InferenceMatching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels
Unlabeled data is often abundant in the clinic, making machine learning methods based on semi-supervised learning a good match for this setting. Despite this, they are currently receiving relatively little attention in m…
DiagnosticMedical Image AnalysisRetinal OCT Disease ClassificationSemi-Supervised Image Classification+1LiDAM: Semi-Supervised Learning with Localized Domain Adaptation and Iterative Matching
Although data is abundant, data labeling is expensive. Semi-supervised learning methods combine a few labeled samples with a large corpus of unlabeled data to effectively train models. This paper introduces our proposed …
Domain AdaptationSemi-Supervised Image ClassificationImplicit Rank-Minimizing Autoencoder
An important component of autoencoders is the method by which the information capacity of the latent representation is minimized or limited. In this work, the rank of the covariance matrix of the codes is implicitly mini…
DecoderImage GenerationRepresentation LearningSemi-Supervised Image ClassificationOnline Semi-Supervised Learning in Contextual Bandits with Episodic Reward
We considered a novel practical problem of online learning with episodically revealed rewards, motivated by several real-world applications, where the contexts are nonstationary over different episodes and the reward fee…
ClusteringDecision MakingMulti-Armed BanditsSelf-Supervised Learning+1