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 4101–4150 of 6661 papers

TitleStatusHype
Contrastive Modules with Temporal Attention for Multi-Task Reinforcement LearningCode0
DyTSCL: Dynamic graph representation via tempo-structural contrastive learningCode0
Rethinking Samples Selection for Contrastive Learning: Mining of Potential Samples—0
On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval—0
Medi-CAT: Contrastive Adversarial Training for Medical Image Classification—0
Uncertainty-guided Boundary Learning for Imbalanced Social Event DetectionCode0
Improving Medical Visual Representations via Radiology Report Generation—0
Towards Generalized Multi-stage Clustering: Multi-view Self-distillation—0
A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content—0
Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning—0
CHAIN: Exploring Global-Local Spatio-Temporal Information for Improved Self-Supervised Video Hashing—0
Adversarial Bootstrapped Question Representation Learning for Knowledge TracingCode0
ReConTab: Regularized Contrastive Representation Learning for Tabular Data—0
Alignment and Outer Shell Isotropy for Hyperbolic Graph Contrastive Learning—0
Image Prior and Posterior Conditional Probability Representation for Efficient Damage Assessment—0
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural NetworksCode0
Boosting Multi-Speaker Expressive Speech Synthesis with Semi-supervised Contrastive Learning—0
IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery—0
DyExplainer: Explainable Dynamic Graph Neural Networks—0
Proposal-Contrastive Pretraining for Object Detection from Fewer Data—0
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation—0
Length is a Curse and a Blessing for Document-level SemanticsCode0
A Diffusion Weighted Graph Framework for New Intent DiscoveryCode0
I^2MD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation—0
Unpaired MRI Super Resolution with Contrastive Learning—0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—0
Contrastive Learning-based Sentence Encoders Implicitly Weight Informative WordsCode0
Topology-aware Debiased Self-supervised Graph Learning for RecommendationCode0
Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector—0
MyriadAL: Active Few Shot Learning for HistopathologyCode0
CalibrationPhys: Self-supervised Video-based Heart and Respiratory Rate Measurements by Calibrating Between Multiple Cameras—0
SAMCLR: Contrastive pre-training on complex scenes using SAM for view sampling—0
MSFormer: A Skeleton-multiview Fusion Method For Tooth Instance Segmentation—0
Joint Searching and Grounding: Multi-Granularity Video Content RetrievalCode0
Remote Heart Rate Monitoring in Smart Environments from Videos with Self-supervised Pre-training—0
Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method—0
CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural TextCode0
TATA: Stance Detection via Topic-Agnostic and Topic-Aware EmbeddingsCode0
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation—0
Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient AlignmentCode0
SILC: Improving Vision Language Pretraining with Self-Distillation—0
DistillCSE: Distilled Contrastive Learning for Sentence EmbeddingsCode0
Spectral-Aware Augmentation for Enhanced Graph Representation Learning—0
Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens—0
Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization—0
Multi-level Contrastive Learning for Script-based Character UnderstandingCode0
Coarse-to-Fine Dual Encoders are Better Frame Identification LearnersCode0
Exploiting Low-confidence Pseudo-labels for Source-free Object Detection—0
WeedCLR: Weed Contrastive Learning through Visual Representations with Class-Optimized Loss in Long-Tailed Datasets—0
Contrastive Learning for Inference in DialogueCode0
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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