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 2751–2775 of 6661 papers

TitleStatusHype
Image Prior and Posterior Conditional Probability Representation for Efficient Damage Assessment—0
Spatio-Temporal Meta Contrastive LearningCode1
Prototypical Contrastive Learning-based CLIP Fine-tuning for Object Re-identificationCode1
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
SSLCL: An Efficient Model-Agnostic Supervised Contrastive Learning Framework for Emotion Recognition in ConversationsCode1
Proposal-Contrastive Pretraining for Object Detection from Fewer Data—0
Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked Auto-EncoderCode1
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation—0
Learning Robust Deep Visual Representations from EEG Brain RecordingsCode1
DyExplainer: Explainable Dynamic Graph Neural Networks—0
Unpaired MRI Super Resolution with Contrastive Learning—0
Length is a Curse and a Blessing for Document-level SemanticsCode0
Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector—0
MyriadAL: Active Few Shot Learning for HistopathologyCode0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—0
Contrastive Learning-based Sentence Encoders Implicitly Weight Informative WordsCode0
CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet ExtractionCode1
I^2MD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation—0
A Diffusion Weighted Graph Framework for New Intent DiscoveryCode0
Topology-aware Debiased Self-supervised Graph Learning for RecommendationCode0
Joint Searching and Grounding: Multi-Granularity Video Content RetrievalCode0
Unveiling the Power of CLIP in Unsupervised Visible-Infrared Person Re-IdentificationCode1
Remote Heart Rate Monitoring in Smart Environments from Videos with Self-supervised Pre-training—0
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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