Papers Semi-Supervised Image Classification
“Semi-Supervised Image Classification” 태그가 달린 논문 169편 · 필터 해제
Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning
Semi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data. To mitigate potentially incorrect pseudo labels, recent frameworks mostly set a fixed confidence threshold to dis…
Semi-Supervised Image ClassificationSimMatchV2: Semi-Supervised Learning with Graph Consistency
Semi-Supervised image classification is one of the most fundamental problem in computer vision, which significantly reduces the need for human labor. In this paper, we introduce a new semi-supervised learning algorithm -…
image-classificationImage ClassificationNode ClassificationSemi-Supervised Image ClassificationNP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation
Semi-supervised semantic segmentation involves assigning pixel-wise labels to unlabeled images at training time. This is useful in a wide range of real-world applications where collecting pixel-wise labels is not feasibl…
image-classificationImage ClassificationSegmentationSelf-Driving Cars+4Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data
We propose UnMixMatch, a semi-supervised learning framework which can learn effective representations from unconstrained unlabelled data in order to scale up performance. Most existing semi-supervised methods rely on the…
Image ClassificationNetwork PruningSemi-Supervised Image ClassificationRelationMatch: Matching In-batch Relationships for Semi-supervised Learning
Semi-supervised learning has achieved notable success by leveraging very few labeled data and exploiting the wealth of information derived from unlabeled data. However, existing algorithms usually focus on aligning predi…
Semi-Supervised Image ClassificationGraph Convolutional Networks based on Manifold Learning for Semi-Supervised Image Classification
Due to a huge volume of information in many domains, the need for classification methods is imperious. In spite of many advances, most of the approaches require a large amount of labeled data, which is often not availabl…
Classificationimage-classificationImage ClassificationSemi-Supervised Image ClassificationVNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution
Since the introduction of deep learning, a wide scope of representation properties, such as decorrelation, whitening, disentanglement, rank, isotropy, and mutual information, have been studied to improve the quality of r…
DisentanglementDomain GeneralizationFew-Shot Image ClassificationGeneral Classification+7NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learning
Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this work, we adjust neural processes (NPs) to t…
Classificationimage-classificationImage ClassificationSemi-Supervised Image ClassificationLearning Customized Visual Models with Retrieval-Augmented Knowledge
Image-text contrastive learning models such as CLIP have demonstrated strong task transfer ability. The high generality and usability of these visual models is achieved via a web-scale data collection process to ensure b…
Contrastive LearningRetrievalSemi-Supervised Image Classificationzero-shot-classification+2Semi-MAE: Masked Autoencoders for Semi-supervised Vision Transformers
Vision Transformer (ViT) suffers from data scarcity in semi-supervised learning (SSL). To alleviate this issue, inspired by masked autoencoder (MAE), which is a data-efficient self-supervised learner, we propose Semi-MAE…
Decoderimage-classificationImage ClassificationRepresentation Learning+1Self Meta Pseudo Labels: Meta Pseudo Labels Without The Teacher
We present Self Meta Pseudo Labels, a novel semi-supervised learning method similar to Meta Pseudo Labels but without the teacher model. We introduce a novel way to use a single model for both generating pseudo labels an…
Semi-Supervised Image ClassificationBeyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework
As a novel distributed learning paradigm, federated learning (FL) faces serious challenges in dealing with massive clients with heterogeneous data distribution and computation and communication resources. Various client-…
Federated Learningimage-classificationImage ClassificationSemi-Supervised Image ClassificationSVFormer: Semi-supervised Video Transformer for Action Recognition
Semi-supervised action recognition is a challenging but critical task due to the high cost of video annotations. Existing approaches mainly use convolutional neural networks, yet current revolutionary vision transformer …
Action Recognitionimage-classificationImage ClassificationSemi-Supervised Image Classification+1Semi-Supervised Single-View 3D Reconstruction via Prototype Shape Priors
The performance of existing single-view 3D reconstruction methods heavily relies on large-scale 3D annotations. However, such annotations are tedious and expensive to collect. Semi-supervised learning serves as an altern…
3D Reconstructionimage-classificationImage ClassificationObject Reconstruction+2OpenMixup: Open Mixup Toolbox and Benchmark for Visual Representation Learning
Mixup augmentation has emerged as a widely used technique for improving the generalization ability of deep neural networks (DNNs). However, the lack of standardized implementations and benchmarks has impeded recent progr…
BenchmarkingClassificationImage ClassificationRepresentation Learning+2USB: A Unified Semi-supervised Learning Benchmark for Classification
Semi-supervised learning (SSL) improves model generalization by leveraging massive unlabeled data to augment limited labeled samples. However, currently, popular SSL evaluation protocols are often constrained to computer…
General ClassificationGPUSemi-Supervised Image ClassificationSemi-supervised Vision Transformers at Scale
We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored topic despite the wide adoption of the ViT architectures to different tasks. To tackle this problem, we propose a new SSL pipeline,…
Inductive BiasSemi-Supervised Image ClassificationRDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning
In this work, we propose Reciprocal Distribution Alignment (RDA) to address semi-supervised learning (SSL), which is a hyperparameter-free framework that is independent of confidence threshold and works with both the mat…
Semi-Supervised Image ClassificationSemi-Supervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework
Deep neural networks (DNNs) show impressive performance for hyperspectral image (HSI) classification when abundant labeled samples are available. The problem is that HSI sample annotation is extremely costly and the budg…
Hyperspectral Image Classificationimage-classificationImage ClassificationPseudo Label+1NP-Match: When Neural Processes meet Semi-Supervised Learning
Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this work, we adjust neural processes (NPs) to t…
image-classificationImage ClassificationSemi-Supervised Image Classification