Papers Semi-Supervised Image Classification
“Semi-Supervised Image Classification” 태그가 달린 논문 169편 · 필터 해제
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization. However, we argue that existing methods …
FairnessSemi-Supervised Image ClassificationDoubleMatch: Improving Semi-Supervised Learning with Self-Supervision
Following the success of supervised learning, semi-supervised learning (SSL) is now becoming increasingly popular. SSL is a family of methods, which in addition to a labeled training set, also use a sizable collection of…
Semi-Supervised Image ClassificationMasked Siamese Networks for Label-Efficient Learning
We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. Our approach matches the representation of an image view containing randomly masked patches to the repres…
image-classificationImage ClassificationSelf-Supervised Image ClassificationSelf-Supervised Learning+1MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization
The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom ful…
Semi-Supervised Image ClassificationSimMatch: Semi-supervised Learning with Similarity Matching
Learning with few labeled data has been a longstanding problem in the computer vision and machine learning research community. In this paper, we introduced a new semi-supervised learning framework, SimMatch, which simult…
Semantic SimilaritySemantic Textual SimilaritySemi-Supervised Image ClassificationClass-Aware Contrastive Semi-Supervised Learning
Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw data utilization. However, its training procedure suffers from confirmation bias due to the noise contained in self-generated artificial…
Image ClassificationPseudo LabelSemi-Supervised Image ClassificationGlobal-Local Regularization Via Distributional Robustness
Despite superior performance in many situations, deep neural networks are often vulnerable to adversarial examples and distribution shifts, limiting model generalization ability in real-world applications. To alleviate t…
Adversarial RobustnessDomain AdaptationDomain GeneralizationSemi-Supervised Image ClassificationVision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
Discriminative self-supervised learning allows training models on any random group of internet images, and possibly recover salient information that helps differentiate between the images. Applied to ImageNet, this leads…
Action ClassificationAction RecognitionCopy DetectionDomain Generalization+13Debiased Self-Training for Semi-Supervised Learning
Deep neural networks achieve remarkable performances on a wide range of tasks with the aid of large-scale labeled datasets. Yet these datasets are time-consuming and labor-exhaustive to obtain on realistic tasks. To miti…
Object RecognitionScene ClassificationSemi-Supervised Image ClassificationTexture ClassificationDeep Reference Priors: What is the best way to pretrain a model?
What is the best way to exploit extra data -- be it unlabeled data from the same task, or labeled data from a related task -- to learn a given task? This paper formalizes the question using the theory of reference priors…
image-classificationSemi-Supervised Image ClassificationTransfer LearningContrastive Regularization for Semi-Supervised Learning
Consistency regularization on label predictions becomes a fundamental technique in semi-supervised learning, but it still requires a large number of training iterations for high performance. In this study, we analyze tha…
Semi-Supervised Image ClassificationPushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
Despite recent progress made by self-supervised methods in representation learning with residual networks, they still underperform supervised learning on the ImageNet classification benchmark, limiting their applicabilit…
image-classificationImage ClassificationLinear evaluationRepresentation Learning+4Debiased Learning from Naturally Imbalanced Pseudo-Labels
Pseudo-labels are confident predictions made on unlabeled target data by a classifier trained on labeled source data. They are widely used for adapting a model to unlabeled data, e.g., in a semi-supervised learning setti…
counterfactualCounterfactual ReasoningFew-Shot Image Classificationimbalanced classification+2An analysis of over-sampling labeled data in semi-supervised learning with FixMatch
Most semi-supervised learning methods over-sample labeled data when constructing training mini-batches. This paper studies whether this common practice improves learning and how. We compare it to an alternative setting w…
image-classificationImage ClassificationSemi-Supervised Image ClassificationTowards Discovering the Effectiveness of Moderately Confident Samples for Semi-Supervised Learning
Semi-supervised learning (SSL) has been studied for a long time to solve vision tasks in data-efficient application scenarios. SSL aims to learn a good classification model using a few labeled data together with larg…
Model OptimizationSemi-Supervised Image ClassificationOpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency Regularization
Semi-supervised learning (SSL) is an effective means to leverage unlabeled data to improve a model’s performance. Typical SSL methods like FixMatch assume that labeled and unlabeled data share the same label space. Howev…
Novelty DetectionOutlier DetectionSemi-Supervised Image ClassificationSemi-Supervised Vision Transformers
We study the training of Vision Transformers for semi-supervised image classification. Transformers have recently demonstrated impressive performance on a multitude of supervised learning tasks. Surprisingly, we show Vis…
image-classificationImage ClassificationInductive BiasSemi-Supervised Image ClassificationiBOT: 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+7Probabilistic Contrastive Learning for Domain Adaptation
Contrastive learning can largely enhance the feature discriminability in a self-supervised manner and has achieved remarkable success for various visual tasks. However, it is undesirably observed that the standard contra…
Contrastive LearningDomain AdaptationImage ClassificationRepresentation Learning+4DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples
The scarcity of labeled data is a critical obstacle to deep learning. Semi-supervised learning (SSL) provides a promising way to leverage unlabeled data by pseudo labels. However, when the size of labeled data is very sm…
Multiple-choiceSemi-Supervised Image Classification