Papers Semi-Supervised Image Classification
“Semi-Supervised Image Classification” 태그가 달린 논문 169편 · 필터 해제
Integrating Large Language Models and Graph Convolutional Networks for Semi-Supervised Image Classification
While the growing availability of image data has driven significant advances, labeling datasets remains costly and time-consuming. Therefore, semi-supervised approaches such as Graph Convolutional Networks (GCNs), which …
Semi-Supervised Image ClassificationSemantic SimilarityGraph Neural Networks for Semi-Supervised Image Classification with Multi-Feature Aggregation
Feature extraction involves the identification and extraction of salient characteristics or patterns, including edges, textures, shapes, and color attributes. Contemporary feature extractors predominantly leverage deep l…
Semi-Supervised Image ClassificationViTSGMM: A Robust Semi-Supervised Image Recognition Network Using Sparse Labels
We present ViTSGMM, an image recognition network that leverages semi-supervised learning in a highly efficient manner. Existing works often rely on complex training techniques and architectures, while their generalizatio…
Semi-Supervised Image ClassificationApplications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning
In recent years, image classification, as a core task in computer vision, relies on high-quality labelled data, which restricts the wide application of deep learning models in practical scenarios. To alleviate the proble…
Classificationimage-classificationImage ClassificationImage Generation+1Simple Semi-supervised Knowledge Distillation from Vision-Language Models via $\mathbf{\texttt{D}}$ual-$\mathbf{\texttt{H}}$ead $\mathbf{\texttt{O}}$ptimization
Vision-language models (VLMs) have achieved remarkable success across diverse tasks by leveraging rich textual information with minimal labeled data. However, deploying such large models remains challenging, particularly…
Few-Shot Image ClassificationKnowledge DistillationSemi-Supervised Image ClassificationSemi-Supervised Image Classification on ImageNet - 10% labeled dataWeakly Semi-supervised Whole Slide Image Classification by Two-level Cross Consistency Supervision
Computer-aided Whole Slide Image (WSI) classification has the potential to enhance the accuracy and efficiency of clinical pathological diagnosis. It is commonly formulated as a Multiple Instance Learning (MIL) problem, …
Classificationimage-classificationImage ClassificationMultiple Instance Learning+1Diff-SySC: An Approach Using Diffusion Models for Semi-Supervised Image Classification
Diffusion models have revolutionized the field of generative machine learning due to their effectiveness in capturing complex, multimodal data distributions. Semi-supervised learning represents a technique that allows th…
image-classificationImage ClassificationSemi-Supervised Image ClassificationSynCo: Synthetic Hard Negatives in Contrastive Learning for Better Unsupervised Visual Representations
Contrastive learning has become a dominant approach in self-supervised visual representation learning. Hard negatives - samples closely resembling the anchor - are key to enhancing learned representations' discriminative…
Contrastive LearningImage ClassificationImage SegmentationInstance Segmentation+8Self Adaptive Threshold Pseudo-labeling and Unreliable Sample Contrastive Loss for Semi-supervised Image Classification
Semi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data. However, pseudo-labeling-based semi-supervised approaches suffer from two problems in image classification: (1) E…
image-classificationImage ClassificationSemi-Supervised Image ClassificationA Method of Moments Embedding Constraint and its Application to Semi-Supervised Learning
Discriminative deep learning models with a linear+softmax final layer have a problem: the latent space only predicts the conditional probabilities $p(Y|X)$ but not the full joint distribution $p(Y,X)$, which necessitates…
image-classificationImage ClassificationOutlier DetectionSemi-Supervised Image Classification+1InfoMatch: Entropy Neural Estimation for Semi-Supervised Image Classification
Semi-supervised image classification, leveraging pseudo supervision and consistency regularization, has demonstrated remarkable success. However, the ongoing challenge lies in fully exploiting the potential of unlabeled …
Contrastive Learningimage-classificationImage ClassificationSemi-Supervised Image ClassificationPseudo-label Learning with Calibrated Confidence Using an Energy-based Model
In pseudo-labeling (PL), which is a type of semi-supervised learning, pseudo-labels are assigned based on the confidence scores provided by the classifier; therefore, accurate confidence is important for successful PL. I…
image-classificationImage ClassificationPseudo LabelSemi-Supervised Image ClassificationColor-$S^{4}L$: Self-supervised Semi-supervised Learning with Image Colorization
This work addresses the problem of semi-supervised image classification tasks with the integration of several effective self-supervised pretext tasks. Different from widely-used consistency regularization within semi-sup…
Colorizationimage-classificationImage ClassificationImage Colorization+1Roll With the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained Learning
While semi-supervised learning (SSL) has yielded promising results, the more realistic SSL scenario remains to be explored, in which the unlabeled data exhibits extremely high recognition difficulty, e.g., fine-grained v…
Fine-Grained Image ClassificationSemi-Supervised Image ClassificationMeta Co-Training: Two Views are Better than One
In many practical computer vision scenarios unlabeled data is plentiful, but labels are scarce and difficult to obtain. As a result, semi-supervised learning which leverages unlabeled data to boost the performance of sup…
Fine-Grained Image Classificationimage-classificationImage ClassificationSemi-Supervised Image ClassificationSequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning
Semi-supervised learning (SSL) has become popular in recent years because it allows the training of a model using a large amount of unlabeled data. However, one issue that many SSL methods face is the confirmation bias, …
Semi-Supervised Image ClassificationSemi-Supervised Image Classification on ImageNet - 10% labeled dataDebiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector
Recent studies on semi-supervised learning (SSL) have achieved great success. Despite their promising performance, current state-of-the-art methods tend toward increasingly complex designs at the cost of introducing more…
Contrastive LearningSemi-Supervised Image ClassificationSemiReward: A General Reward Model for Semi-supervised Learning
Semi-supervised learning (SSL) has witnessed great progress with various improvements in the self-training framework with pseudo labeling. The main challenge is how to distinguish high-quality pseudo labels against the c…
Few-Shot Image ClassificationImage ClassificationPseudo LabelSemi-supervised Audio Classification+3Towards Semi-supervised Learning with Non-random Missing Labels
Semi-supervised learning (SSL) tackles the label missing problem by enabling the effective usage of unlabeled data. While existing SSL methods focus on the traditional setting, a practical and challenging scenario called…
Semi-Supervised Image ClassificationHow To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning
Do we need active learning? The rise of strong deep semi-supervised methods raises doubt about the usability of active learning in limited labeled data settings. This is caused by results showing that combining semi-supe…
Active Learningimage-classificationImage ClassificationSemi-Supervised Image Classification