SOTAVerified

Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

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Showing 2901–2950 of 6661 papers

TitleStatusHype
Adaptive H&E-IHC information fusion staining framework based on feature extraCode0
Cross-Model Cross-Stream Learning for Self-Supervised Human Action RecognitionCode0
Joint Prediction of Meningioma Grade and Brain Invasion via Task-Aware Contrastive LearningCode0
JCSE: Contrastive Learning of Japanese Sentence Embeddings and Its ApplicationsCode0
Heterogeneous Network Based Contrastive Learning Method for PolSAR Land Cover ClassificationCode0
Heterogeneous network drug-target interaction prediction model based on graph wavelet transform and multi-level contrastive learningCode0
Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIPCode0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
JIGMARK: A Black-Box Approach for Enhancing Image Watermarks against Diffusion Model EditsCode0
Heterogeneous Tri-stream Clustering NetworkCode0
Enhanced Long-Tailed Recognition with Contrastive CutMix AugmentationCode0
IRConStyle: Image Restoration Framework Using Contrastive Learning and Style TransferCode0
Joint Representation Learning for Text and 3D Point CloudCode0
ENGAGE: Explanation Guided Data Augmentation for Graph Representation LearningCode0
Self-supervised Multi-modal Training from Uncurated Image and Reports Enables Zero-shot Oversight Artificial Intelligence in RadiologyCode0
End-to-End Supervised Multilabel Contrastive LearningCode0
Constructing Contrastive samples via Summarization for Text Classification with limited annotationsCode0
HaSa: Hardness and Structure-Aware Contrastive Knowledge Graph EmbeddingCode0
Intra-video Positive Pairs in Self-Supervised Learning for UltrasoundCode0
Intra- and Inter-modal Context Interaction Modeling for Conversational Speech SynthesisCode0
Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited PlacesCode0
Intrinsic and Extrinsic Factor Disentanglement for Recommendation in Various Context ScenariosCode0
Rethinking 360deg Image Visual Attention Modelling With Unsupervised Learning.Code0
Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology DetectionCode0
A Vlogger-augmented Graph Neural Network Model for Micro-video RecommendationCode0
Interventional Video Grounding with Dual Contrastive LearningCode0
A Token-level Contrastive Framework for Sign Language TranslationCode0
Intermediate Domain-guided Adaptation for Unsupervised Chorioallantoic Membrane Vessel SegmentationCode0
EMS: Efficient and Effective Massively Multilingual Sentence Embedding LearningCode0
Interactive Dimensionality Reduction for Comparative AnalysisCode0
A Vision-Language Foundation Model for Leaf Disease IdentificationCode0
Integrating Deep Metric Learning with Coreset for Active Learning in 3D SegmentationCode0
Consistency of augmentation graph and network approximability in contrastive learningCode0
Intensity-Spatial Dual Masked Autoencoder for Multi-Scale Feature Learning in Chest CT SegmentationCode0
Text Conditioned Symbolic Drumbeat Generation using Latent Diffusion ModelsCode0
GaussianStyle: Gaussian Head Avatar via StyleGANCode0
EMC^2: Efficient MCMC Negative Sampling for Contrastive Learning with Global ConvergenceCode0
Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for RecommendationsCode0
Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from ImagesCode0
IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature RepresentationCode0
Information-Maximized Soft Variable Discretization for Self-Supervised Image Representation LearningCode0
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantificationCode0
ConPro: Learning Severity Representation for Medical Images using Contrastive Learning and Preference OptimizationCode0
Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional NetworksCode0
Contrastive Learning of Sociopragmatic Meaning in Social MediaCode0
Incorporating Task-specific Concept Knowledge into Script LearningCode0
Incorporating Domain Knowledge Graph into Multimodal Movie Genre Classification with Self-Supervised Attention and Contrastive LearningCode0
Connect Later: Improving Fine-tuning for Robustness with Targeted AugmentationsCode0
AutoSSVH: Exploring Automated Frame Sampling for Efficient Self-Supervised Video HashingCode0
Incomplete Contrastive Multi-View Clustering with High-Confidence GuidingCode0
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All datasetsimagenet-1k10,000 People - Human Pose Recognition DataCIFAR-10STL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6—Unverified
2ResNet50ImageNet Top-1 Accuracy73—Unverified
3ResNet50ImageNet Top-1 Accuracy71.1—Unverified
4ResNet50ImageNet Top-1 Accuracy69.3—Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6—Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8—Unverified
7ResNet50ImageNet Top-1 Accuracy63.6—Unverified
8ResNet50ImageNet Top-1 Accuracy61.5—Unverified
9ResNet50ImageNet Top-1 Accuracy61.5—Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3—Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55—Unverified