Semi-Supervised Image Classification
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Benchmarks
ImageNet - 10% labeled data
ImageNet - 1% labeled data
CIFAR-10, 4000 Labels
cifar-100, 10000 Labels
CIFAR-10, 250 Labels
CIFAR-10, 40 Labels
CIFAR-100, 400 Labels
SVHN, 1000 labels
CIFAR-100, 2500 Labels
SVHN, 250 Labels
STL-10, 1000 Labels
CIFAR-10, 1000 Labels
SVHN, 500 Labels
SVHN, 40 Labels
CIFAR-10, 2000 Labels
STL-10, 40 Labels
cifar10, 250 Labels
CIFAR-10, 20 Labels
STL-10
cifar-10, 10 Labels
CIFAR-10, 80 Labels
CIFAR-100, 5000Labels
CIFAR-10, 100 Labels
CIFAR-10, 30 Labels
CIFAR-10, 500 Labels
CIFAR-100, 1000 Labels
CIFAR-100, 200 Labels
CIFAR-100, 4000 Labels
CIFAR-100, 5000 Labels
Caltech-101
Caltech-101, 202 Labels
Caltech-256
Caltech-256, 1024 Labels
DeepWeeds, 99 Labels
EuroSAT, 100 Labels
EuroSAT, 20 Labels
Imagenette, 100 Labels
Imagenette, 20 Labels
STL-10, 5000 Labels
SVHN, 2000 Labels
SVHN, 4000 Labels
Salinas
Most implemented
A Simple Framework for Contrastive Learning of Visual Representations
Learning Transferable Visual Models From Natural Language Supervision
mixup: Beyond Empirical Risk Minimization
Improved Techniques for Training GANs
Bootstrap your own latent: A new approach to self-supervised Learning
MixMatch: A Holistic Approach to Semi-Supervised Learning
Papers
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+1