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 2651–2675 of 6661 papers

TitleStatusHype
Co-guiding for Multi-intent Spoken Language Understanding—0
Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning—0
Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential RecommendationCode0
FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection—0
AudioLog: LLMs-Powered Long Audio Logging with Hybrid Token-Semantic Contrastive LearningCode0
A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series—0
Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive LearningCode0
BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive LearningCode1
OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contrastive LearningCode1
Unearthing Common Inconsistency for Generalisable Deepfake Detection—0
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding—0
Self-Distilled Representation Learning for Time Series—0
Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain SolutionCode1
Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion RecognitionCode1
Lesion Search with Self-supervised Learning—0
Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training—0
FOAL: Fine-grained Contrastive Learning for Cross-domain Aspect Sentiment Triplet Extraction—0
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection—0
Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis—0
From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning—0
Robust Contrastive Learning With Theory Guarantee—0
MoCo-Transfer: Investigating out-of-distribution contrastive learning for limited-data domains—0
Towards Generalizable SER: Soft Labeling and Data Augmentation for Modeling Temporal Emotion Shifts in Large-Scale Multilingual SpeechCode0
Correlation-aware active learning for surgery video segmentation—0
Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person SearchCode0
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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