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 3851–3900 of 6661 papers

TitleStatusHype
A Global and Patch-wise Contrastive Loss for Accurate Automated Exudate DetectionCode0
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention—0
Unpaired Translation from Semantic Label Maps to Images by Leveraging Domain-Specific Simulations—0
A General-Purpose Transferable Predictor for Neural Architecture Search—0
Multi-Modal Self-Supervised Learning for RecommendationCode2
Mask-guided BERT for Few Shot Text Classification—0
Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning—0
DrasCLR: A Self-supervised Framework of Learning Disease-related and Anatomy-specific Representation for 3D Medical Images—0
Generalization Bounds for Adversarial Contrastive Learning—0
Heterogeneous Social Event Detection via Hyperbolic Graph RepresentationsCode0
Pseudo Contrastive Learning for Graph-based Semi-supervised Learning—0
Supervised Contrastive Learning and Feature Fusion for Improved Kinship Verification—0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the LeastCode1
EnfoMax: Domain Entropy and Mutual Information Maximization for Domain Generalized Face Anti-spoofing—0
Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks—0
Like a Good Nearest Neighbor: Practical Content Moderation and Text ClassificationCode1
Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton SequencesCode1
Bridge the Gap between Language models and Tabular Understanding—0
LightGCL: Simple Yet Effective Graph Contrastive Learning for RecommendationCode2
LabelPrompt: Effective Prompt-based Learning for Relation Classification—0
Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking—0
CluCDD:Contrastive Dialogue Disentanglement via ClusteringCode1
Audio-Visual Contrastive Learning with Temporal Self-Supervision—0
How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval—0
Multi-Source Contrastive Learning from Musical AudioCode1
Symbolic Discovery of Optimization AlgorithmsCode0
CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D DatasetsCode1
Anti-Compression Contrastive Facial Forgery Detection—0
Type-Aware Decomposed Framework for Few-Shot Named Entity RecognitionCode1
Imitation from Observation With Bootstrapped Contrastive Learning—0
ContrasInver: Ultra-Sparse Label Semi-supervised Regression for Multi-dimensional Seismic Inversion—0
Understanding Multimodal Contrastive Learning and Incorporating Unpaired DataCode0
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading—0
Contrastive Learning and the Emergence of Attributes Associations—0
Generalized Few-Shot Continual Learning with Contrastive Mixture of AdaptersCode1
LipLearner: Customizable Silent Speech Interactions on Mobile DevicesCode1
Self-supervised pseudo-colorizing of masked cellsCode0
Multispectral Contrastive Learning with Viewmaker NetworksCode0
Compositional Exemplars for In-context LearningCode1
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System PredictionCode0
HateProof: Are Hateful Meme Detection Systems really Robust?—0
Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image AnalysisCode1
Fairness-aware Multi-view ClusteringCode0
Analyzing Multimodal Objectives Through the Lens of Generative Diffusion Guidance—0
End-to-end Semantic Object Detection with Cross-Modal Alignment—0
ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification—0
Self-Supervised Node Representation Learning via Node-to-Neighbourhood AlignmentCode1
Deep Intra-Image Contrastive Learning for Weakly Supervised One-Step Person SearchCode0
Detecting Contextomized Quotes in News Headlines by Contrastive LearningCode0
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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