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 3051–3075 of 6661 papers

TitleStatusHype
Resolving Lexical Bias in Edit Scoping with Projector Editor NetworksCode0
Leveraging Superfluous Information in Contrastive Representation Learning—0
Uniting contrastive and generative learning for event sequences models—0
Deep Code Search with Naming-Agnostic Contrastive Multi-View Learning—0
3C: Confidence-Guided Clustering and Contrastive Learning for Unsupervised Person Re-IdentificationCode0
WPN: An Unlearning Method Based on N-pair Contrastive Learning in Language Models—0
ConVerSum: A Contrastive Learning-based Approach for Data-Scarce Solution of Cross-Lingual Summarization Beyond Direct Equivalents—0
Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive Hashing—0
V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models—0
Enhancing Audio-Language Models through Self-Supervised Post-Training with Text-Audio PairsCode0
EEG-SCMM: Soft Contrastive Masked Modeling for Cross-Corpus EEG-Based Emotion Recognition—0
Don't Click the Bait: Title Debiasing News Recommendation via Cross-Field Contrastive Learning—0
ChatZero:Zero-shot Cross-Lingual Dialogue Generation via Pseudo-Target Language—0
Focus on Focus: Focus-oriented Representation Learning and Multi-view Cross-modal Alignment for Glioma GradingCode0
DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System—0
Time-Dependent VAE for Building Latent Representations from Visual Neural Activity with Complex Dynamics—0
HyperTaxel: Hyper-Resolution for Taxel-Based Tactile Signals Through Contrastive Learning—0
Dual-Domain CLIP-Assisted Residual Optimization Perception Model for Metal Artifact Reduction—0
Evidential Graph Contrastive Alignment for Source-Free Blending-Target Domain Adaptation—0
End-to-end Semantic-centric Video-based Multimodal Affective Computing—0
Class-aware and Augmentation-free Contrastive Learning from Label Proportion—0
Bundle Recommendation with Item-level Causation-enhanced Multi-view LearningCode0
Contrastive Learning on Medical Intents for Sequential Prescription Recommendation—0
Oracle Bone Script Similiar Character Screening Approach Based on Simsiam Contrastive Learning and Supervised Learning—0
DEEPTalk: Dynamic Emotion Embedding for Probabilistic Speech-Driven 3D Face Animation—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