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 5426–5450 of 6661 papers

TitleStatusHype
Large-Scale Unsupervised Person Re-Identification with Contrastive Learning—0
LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding—0
Latent Prompt Tuning for Text Summarization—0
Latent Space Consistency for Sparse-View CT Reconstruction—0
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning—0
LAVA: Language Audio Vision Alignment for Contrastive Video Pre-Training—0
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity—0
Learnable Sequence Augmenter for Triplet Contrastive Learning in Sequential Recommendation—0
Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning—0
Learn Beneficial Noise as Graph Augmentation—0
Synthetic Data Can Also Teach: Synthesizing Effective Data for Unsupervised Visual Representation Learning—0
LEARNER: Learning Granular Labels from Coarse Labels using Contrastive Learning—0
Learn from Real: Reality Defender's Submission to ASVspoof5 Challenge—0
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss—0
Linguistic Query-Guided Mask Generation for Referring Image Segmentation—0
Learning Backdoors for Mixed Integer Linear Programs with Contrastive Learning—0
Learning beyond sensations: how dreams organize neuronal representations—0
Learning Contrastive Representation for Semantic Correspondence—0
Learning Contrastive Self-Distillation for Ultra-Fine-Grained Visual Categorization Targeting Limited Samples—0
Learning Co-Speech Gesture Representations in Dialogue through Contrastive Learning: An Intrinsic Evaluation—0
Learning crop type mapping from regional label proportions in large-scale SAR and optical imagery—0
Learning Cross-modal Contrastive Features for Video Domain Adaptation—0
Learning Decoupled Retrieval Representation for Nearest Neighbour Neural Machine Translation—0
Learning Deep Representations via Contrastive Learning for Instance Retrieval—0
Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing—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