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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3326–3350 of 6661 papers

TitleStatusHype
Understanding the Role of Nonlinearity in Training Dynamics of Contrastive Learning—0
Underwater-Art: Expanding Information Perspectives With Text Templates For Underwater Acoustic Target Recognition—0
Unearthing Common Inconsistency for Generalisable Deepfake Detection—0
UniBind: LLM-Augmented Unified and Balanced Representation Space to Bind Them All—0
UniCL: A Universal Contrastive Learning Framework for Large Time Series Models—0
Bidirectional Contrastive Split Learning for Visual Question Answering—0
UNICON: Unsupervised Intent Discovery via Semantic-level Contrastive Learning—0
UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning—0
Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control—0
UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning—0
Unified Contrastive Fusion Transformer for Multimodal Human Action Recognition—0
Unified Loss of Pair Similarity Optimization for Vision-Language Retrieval—0
Unified Medical Image-Text-Label Contrastive Learning With Continuous Prompt—0
Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning—0
Unified Representation Space for 3D Visual Grounding—0
UniForensics: Face Forgery Detection via General Facial Representation—0
Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search—0
Unifying Graph Contrastive Learning via Graph Message Augmentation—0
Unifying Search and Recommendation: A Generative Paradigm Inspired by Information Theory—0
Unifying Structure and Language Semantic for Efficient Contrastive Knowledge Graph Completion with Structured Entity Anchors—0
Unifying Vision-Language Representation Space with Single-tower Transformer—0
UniHPE: Towards Unified Human Pose Estimation via Contrastive Learning—0
Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection—0
UniLoc: Towards Universal Place Recognition Using Any Single Modality—0
Uni-Mlip: Unified Self-supervision for Medical Vision Language Pre-training—0
Show:102550
← PrevPage 134 of 267Next →
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