Papers Semi-Supervised Image Classification
“Semi-Supervised Image Classification” 태그가 달린 논문 169편 · 필터 해제
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
The recently proposed FixMatch achieved state-of-the-art results on most semi-supervised learning (SSL) benchmarks. However, like other modern SSL algorithms, FixMatch uses a pre-defined constant threshold for all classe…
Semi-Supervised Image ClassificationSelf-Supervised Learning by Estimating Twin Class Distributions
We present TWIST, a simple and theoretically explainable self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way. We employ a siamese network terminated by a soft…
Fine-Grained Image ClassificationImage ClassificationRepresentation LearningSelf-Supervised Image Classification+4Weakly Supervised Contrastive Learning
Unsupervised visual representation learning has gained much attention from the computer vision community because of the recent achievement of contrastive learning. Most of the existing contrastive learning frameworks ado…
Contrastive LearningRepresentation LearningSelf-Supervised Image ClassificationSemi-Supervised Image ClassificationTransfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image Classification
Deep neural networks produce state-of-the-art results when trained on a large number of labeled examples but tend to overfit when small amounts of labeled examples are used for training. Creating a large number of labele…
image-classificationImage ClassificationMetric LearningSelf-Learning+2Dash: Semi-Supervised Learning with Dynamic Thresholding
While semi-supervised learning (SSL) has received tremendous attentions in many machine learning tasks due to its successful use of unlabeled data, existing SSL algorithms use either all unlabeled examples or the unlabel…
Semi-Supervised Image ClassificationSemi-supervised Image Classification with Grad-CAM Consistency
Consistency training, which exploits both supervised and unsupervised learning with different augmentations on image, is an effective method of utilizing unlabeled data in semi-supervised learning (SSL) manner. Here, we …
Classificationimage-classificationImage ClassificationSemi-Supervised Image ClassificationA New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms
Semi-supervised image classification has shown substantial progress in learning from limited labeled data, but recent advances remain largely untested for clinical applications. Motivated by the urgent need to improve ti…
image-classificationImage ClassificationSemi-Supervised Image ClassificationSemi-Supervised Learning with Multi-Head Co-Training
Co-training, extended from self-training, is one of the frameworks for semi-supervised learning. Without natural split of features, single-view co-training works at the cost of training extra classifiers, where the algor…
Data AugmentationDiversityImage ClassificationSemi-Supervised Image ClassificationSelf-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification
The goal is to use Wasserstein metric to provide pseudo labels for the unlabeled images to train a Convolutional Neural Networks (CNN) in a Semi-Supervised Learning (SSL) manner for the classification task. The basic…
Classificationimage-classificationImage ClassificationPseudo Label+2DASO: Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learning
The capability of the traditional semi-supervised learning (SSL) methods is far from real-world application due to severely biased pseudo-labels caused by (1) class imbalance and (2) class distribution mismatch between l…
imbalanced classificationPseudo LabelSemi-Supervised Image ClassificationLaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semi-Supervised Classification
Semi-supervised learning has received a lot of recent attention as it alleviates the need for large amounts of labelled data which can often be expensive, requires expert knowledge and be time consuming to collect. Recen…
Semi-Supervised Image ClassificationDiffusion-Based Representation Learning
Diffusion-based methods represented as stochastic differential equations on a continuous-time domain have recently proven successful as a non-adversarial generative model. Training such models relies on denoising score m…
Denoisingimage-classificationImage ClassificationRepresentation Learning+1Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries
The proliferation of optical, electron, and scanning probe microscopies gives rise to large volumes of imaging data of objects as diversified as cells, bacteria, pollen, to nanoparticles and atoms and molecules. In most …
Image ClassificationRepresentation LearningSemi-Supervised Image ClassificationVICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Recent self-supervised methods for image representation learning are based on maximizing the agreement between embedding vectors from different views of the same image. A trivial solution is obtained when the encoder out…
Representation LearningSelf-Supervised Image ClassificationSelf-Supervised LearningSemi-Supervised Image ClassificationWith a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations
Self-supervised learning algorithms based on instance discrimination train encoders to be invariant to pre-defined transformations of the same instance. While most methods treat different views of the same image as posit…
Contrastive LearningFine-Grained Image ClassificationImage ClassificationSelf-Supervised Image Classification+3Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples
This paper proposes a novel method of learning by predicting view assignments with support samples (PAWS). The method trains a model to minimize a consistency loss, which ensures that different views of the same unlabele…
Image ClassificationPseudo LabelSemi-Supervised Image ClassificationAll Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training
Pseudo-labeling is a key component in semi-supervised learning (SSL). It relies on iteratively using the model to generate artificial labels for the unlabeled data to train against. A common property among its various me…
AllSemi-Supervised Image ClassificationSimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification
A common classification task situation is where one has a large amount of data available for training, but only a small portion is annotated with class labels. The goal of semi-supervised training, in this context, is to…
ClassificationGeneral ClassificationPseudo LabelSemi-Supervised Image Classification+1Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation
Domain adaptation is to transfer the shared knowledge learned from the source domain to a new environment, i.e., target domain. One common practice is to train the model on both labeled source-domain data and unlabeled t…
Domain AdaptationScene SegmentationSemantic SegmentationSemi-Supervised Image Classification+2Barlow Twins: Self-Supervised Learning via Redundancy Reduction
Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large computer vision benchmarks. A successful approach to SSL is to learn embeddings which are invariant to distortions of the input s…
General ClassificationImage ClassificationObject DetectionSelf-Supervised Image Classification+2