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 3151–3175 of 6661 papers

TitleStatusHype
DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation—0
DR-BFR: Degradation Representation with Diffusion Models for Blind Face Restoration—0
COIN: Contrastive Identifier Network for Breast Mass Diagnosis in Mammography—0
DrasCLR: A Self-supervised Framework of Learning Disease-related and Anatomy-specific Representation for 3D Medical Images—0
DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers—0
DRACO-DehazeNet: An Efficient Image Dehazing Network Combining Detail Recovery and a Novel Contrastive Learning Paradigm—0
DP-CRE: Continual Relation Extraction via Decoupled Contrastive Learning and Memory Structure Preservation—0
Cohere3D: Exploiting Temporal Coherence for Unsupervised Representation Learning of Vision-based Autonomous Driving—0
Co-guiding for Multi-intent Spoken Language Understanding—0
Spectral-Aware Augmentation for Enhanced Graph Representation Learning—0
DPCL-Diff: The Temporal Knowledge Graph Reasoning Based on Graph Node Diffusion Model with Dual-Domain Periodic Contrastive Learning—0
Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method—0
CognitiveNet: Enriching Foundation Models with Emotions and Awareness—0
Double Gradient Reversal Network for Single-Source Domain Generalization in Multi-mode Fault Diagnosis—0
CoDo: Contrastive Learning with Downstream Background Invariance for Detection—0
Double Banking on Knowledge: Customized Modulation and Prototypes for Multi-Modality Semi-supervised Medical Image Segmentation—0
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning—0
Augmented Contrastive Self-Supervised Learning for Audio Invariant Representations—0
Don't Click the Bait: Title Debiasing News Recommendation via Cross-Field Contrastive Learning—0
Do More Negative Samples Necessarily Hurt in Contrastive Learning?—0
Domain Prompt Learning with Quaternion Networks—0
CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search—0
Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data—0
CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search—0
Code Representation Learning At Scale—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