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 2051–2100 of 6661 papers

TitleStatusHype
MSVQ: Self-Supervised Learning with Multiple Sample Views and QueuesCode0
CLMB: deep contrastive learning for robust metagenomic binningCode0
M(otion)-mode Based Prediction of Ejection Fraction using EchocardiogramsCode0
MPCODER: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation LearningCode0
MTS-LOF: Medical Time-Series Representation Learning via Occlusion-Invariant FeaturesCode0
Design of the topology for contrastive visual-textual alignmentCode0
Motif-Centric Representation Learning for Symbolic MusicCode0
Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-trainingCode0
Description-Enhanced Label Embedding Contrastive Learning for Text ClassificationCode0
MOOSS: Mask-Enhanced Temporal Contrastive Learning for Smooth State Evolution in Visual Reinforcement LearningCode0
Morality is Non-Binary: Building a Pluralist Moral Sentence Embedding Space using Contrastive LearningCode0
3SD: Self-Supervised Saliency Detection With No LabelsCode0
MuDAF: Long-Context Multi-Document Attention Focusing through Contrastive Learning on Attention HeadsCode0
Multi-Level Contrastive Learning for Dense Prediction TaskCode0
Molecular Graph Contrastive Learning with Line GraphCode0
A Task-oriented Dialog Model with Task-progressive and Policy-aware Pre-trainingCode0
MaCLR: Motion-aware Contrastive Learning of Representations for VideosCode0
Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal VideosCode0
Modular Sentence Encoders: Separating Language Specialization from Cross-Lingual AlignmentCode0
MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker JointsCode0
Demonstrating and Reducing Shortcuts in Vision-Language Representation LearningCode0
Modeling the Relative Visual Tempo for Self-supervised Skeleton-based Action RecognitionCode0
Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling LawsCode0
MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image AnalysisCode0
DELTA: Decoupling Long-Tailed Online Continual LearningCode0
Model Editing for LLMs4Code: How Far are We?Code0
DELAN: Dual-Level Alignment for Vision-and-Language Navigation by Cross-Modal Contrastive LearningCode0
Model-Contrastive Learning for Backdoor DefenseCode0
Acoustic word embeddings for zero-resource languages using self-supervised contrastive learning and multilingual adaptationCode0
Model-Aware Contrastive Learning: Towards Escaping the DilemmasCode0
Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal LearningCode0
Deep Unsupervised Learning for 3D ALS Point Cloud Change DetectionCode0
DeepRLI: A Multi-objective Framework for Universal Protein--Ligand Interaction PredictionCode0
Multi-level Contrastive Learning for Script-based Character UnderstandingCode0
MVMR: A New Framework for Evaluating Faithfulness of Video Moment Retrieval against Multiple DistractorsCode0
Mix-Domain Contrastive Learning for Unpaired H&E-to-IHC Stain TranslationCode0
Deep Learning for Forensic Identification of SourceCode0
Mitigating Negative Style Transfer in Hybrid Dialogue SystemCode0
A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target InteractionCode0
Deep Intra-Image Contrastive Learning for Weakly Supervised One-Step Person SearchCode0
Mitigating Data Imbalance and Representation Degeneration in Multilingual Machine TranslationCode0
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive SummarizationCode0
DeepIFSAC: Deep Imputation of Missing Values Using Feature and Sample Attention within Contrastive FrameworkCode0
Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited LabelsCode0
Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud RegistrationCode0
Deep Double Self-Expressive Subspace ClusteringCode0
CLIC: Contrastive Learning Framework for Unsupervised Image Complexity RepresentationCode0
MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot LearningCode0
CLHA: A Simple yet Effective Contrastive Learning Framework for Human AlignmentCode0
MG-3D: Multi-Grained Knowledge-Enhanced 3D Medical Vision-Language Pre-trainingCode0
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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