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 4401–4450 of 6661 papers

TitleStatusHype
ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning—0
Legal Element-oriented Modeling with Multi-view Contrastive Learning for Legal Case Retrieval—0
Improving Dense Contrastive Learning with Dense Negative Pairs—0
Learning to Locate Visual Answer in Video Corpus Using QuestionCode0
Pre-Training Representations of Binary Code Using Contrastive Learning—0
Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural NetworksCode1
Contrastive Trajectory Similarity Learning with Dual-Feature AttentionCode1
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative LearningCode0
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training ModelCode1
AdsCVLR: Commercial Visual-Linguistic Representation Modeling in Sponsored Search—0
Robust Diversified Graph Contrastive Network for Incomplete Multi-view ClusteringCode0
Multilingual Representation Distillation with Contrastive Learning—0
Improving Continual Relation Extraction through Prototypical Contrastive Learning—0
Using Whole Slide Image Representations from Self-Supervised Contrastive Learning for Melanoma Concordance Regression—0
Contrastive Bayesian Analysis for Deep Metric LearningCode1
SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link PredictionCode1
Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER—0
Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive LearningCode1
CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification—0
HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model PretrainingCode1
Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive LearningCode0
InfoCSE: Information-aggregated Contrastive Learning of Sentence EmbeddingsCode1
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation LearningCode0
Hierarchical Few-Shot Object Detection: Problem, Benchmark and MethodCode1
Revisiting Self-Supervised Contrastive Learning for Facial Expression RecognitionCode1
SAICL: Student Modelling with Interaction-level Auxiliary Contrastive Tasks for Knowledge Tracing and Dropout Prediction—0
Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning—0
Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information—0
Augmentations in Hypergraph Contrastive Learning: Fabricated and GenerativeCode1
Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD CodingCode1
Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup—0
Brief Introduction to Contrastive Learning Pretext Tasks for Visual Representation—0
Uncovering the Structural Fairness in Graph Contrastive LearningCode1
Data Augmentation-free Unsupervised Learning for 3D Point Cloud UnderstandingCode1
Content-Based Search for Deep Generative ModelsCode2
CLAD: A Contrastive Learning based Approach for Background DebiasingCode0
Making Your First Choice: To Address Cold Start Problem in Vision Active LearningCode1
Jitter Does Matter: Adapting Gaze Estimation to New Domains—0
Revisiting Graph Contrastive Learning from the Perspective of Graph SpectrumCode1
CFL-Net: Image Forgery Localization Using Contrastive LearningCode0
When and why vision-language models behave like bags-of-words, and what to do about it?Code2
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions—0
Supervised Metric Learning to Rank for Retrieval via Contextual Similarity OptimizationCode1
Self-supervised Pre-training for Semantic Segmentation in an Indoor Scene—0
A Study on the Efficiency and Generalization of Light Hybrid Retrievers—0
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive LearningCode0
Backdoor Attacks in the Supply Chain of Masked Image Modeling—0
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud SegmentationCode1
ContraCLM: Contrastive Learning For Causal Language ModelCode1
CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingCode1
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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