Papers Semi-Supervised Image Classification
“Semi-Supervised Image Classification” 태그가 달린 논문 169편 · 필터 해제
Unsupervised 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 ClassificationMulti-Task Curriculum Framework for Open-Set Semi-Supervised Learning
Semi-supervised learning (SSL) has been proposed to leverage unlabeled data for training powerful models when only limited labeled data is available. While existing SSL methods assume that samples in the labeled and unla…
Semi-Supervised Image ClassificationFeatMatch: Feature-Based Augmentation for Semi-Supervised Learning
Recent state-of-the-art semi-supervised learning (SSL) methods use a combination of image-based transformations and consistency regularization as core components. Such methods, however, are limited to simple transformati…
ClusteringData AugmentationSemi-Supervised Image ClassificationImproving Face Recognition by Clustering Unlabeled Faces in the Wild
While deep face recognition has benefited significantly from large-scale labeled data, current research is focused on leveraging unlabeled data to further boost performance, reducing the cost of human annotation. Prior w…
ClusteringFace ClusteringFace RecognitionOut-of-Distribution Detection+1Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection
This paper focuses on Semi-Supervised Object Detection (SSOD). Knowledge Distillation (KD) has been widely used for semi-supervised image classification. However, adapting these methods for SSOD has the following obstacl…
image-classificationImage ClassificationKnowledge DistillationObject+5Consistency Regularization with Generative Adversarial Networks for Semi-Supervised Learning
Generative Adversarial Networks (GANs) based semi-supervised learning (SSL) approaches are shown to improve classification performance by utilizing a large number of unlabeled samples in conjunction with limited labeled …
image-classificationImage ClassificationSemi-Supervised Image ClassificationBig Self-Supervised Models are Strong Semi-Supervised Learners
One paradigm for learning from few labeled examples while making best use of a large amount of unlabeled data is unsupervised pretraining followed by supervised fine-tuning. Although this paradigm uses unlabeled data in …
Self-Supervised Image ClassificationSemi-Supervised Image ClassificationUnsupervised Learning of Visual Features by Contrasting Cluster Assignments
Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and…
Contrastive LearningData AugmentationImage ClassificationSelf-Supervised Image Classification+1Building One-Shot Semi-supervised (BOSS) Learning up to Fully Supervised Performance
Reaching the performance of fully supervised learning with unlabeled data and only labeling one sample per class might be ideal for deep learning applications. We demonstrate for the first time the potential for building…
Semi-Supervised Image ClassificationBootstrap your own latent: A new approach to self-supervised Learning
We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target networks, that interact and learn from …
Image ClassificationLinear evaluationPerson Re-IdentificationRepresentation Learning+4SCAN: 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+5A Self-ensembling Framework for Semi-supervised Knee Cartilage Defects Assessment with Dual-Consistency
Knee osteoarthritis (OA) is one of the most common musculoskeletal disorders and requires early-stage diagnosis. Nowadays, the deep convolutional neural networks have achieved greatly in the computer-aided diagnosis fiel…
ClassificationGeneral ClassificationLesion ClassificationSemi-Supervised Image ClassificationPrototypical Contrastive Learning of Unsupervised Representations
This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that addresses the fundamental limitations of instance-wise contrastive learning. PCL not only learns low-level …
ClusteringContrastive LearningRepresentation LearningSelf-Supervised Image Classification+2DMT: Dynamic Mutual Training for Semi-Supervised Learning
Recent semi-supervised learning methods use pseudo supervision as core idea, especially self-training methods that generate pseudo labels. However, pseudo labels are unreliable. Self-training methods usually rely on sing…
image-classificationImage ClassificationPseudo LabelSemantic Segmentation+2Milking CowMask for Semi-Supervised Image Classification
Consistency regularization is a technique for semi-supervised learning that underlies a number of strong results for classification with few labeled data. It works by encouraging a learned model to be robust to perturbat…
ClassificationGeneral Classificationimage-classificationImage Classification+1Meta Pseudo Labels
We present Meta Pseudo Labels, a semi-supervised learning method that achieves a new state-of-the-art top-1 accuracy of 90.2% on ImageNet, which is 1.6% better than the existing state-of-the-art. Like Pseudo Labels, Meta…
Image ClassificationMeta-LearningSemi-Supervised Image ClassificationA Simple Framework for Contrastive Learning of Visual Representations
This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures…
Contrastive LearningImage ClassificationObject RecognitionPerson Re-Identification+4Subspace Capsule Network
Convolutional neural networks (CNNs) have become a key asset to most of fields in AI. Despite their successful performance, CNNs suffer from a major drawback. They fail to capture the hierarchy of spatial relation among …
General ClassificationGenerative Adversarial Networkimage-classificationImage Classification+2FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. In this paper, we demonstrate the power of a simple combination of two common SSL methods: consist…
Image ClassificationPseudo LabelSemi-Supervised Image Classificationbatchboost: regularization for stabilizing training with resistance to underfitting & overfitting
Overfitting & underfitting and stable training are an important challenges in machine learning. Current approaches for these issues are mixup, SamplePairing and BC learning. In our work, we state the hypothesis that mixi…
Image ClassificationSemi-Supervised Image Classification